{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e3c308d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import glob\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9dbb0de",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Utility functions and classes for cross species\n",
    "analysis\n",
    "\n",
    "@yhr91\n",
    "\"\"\"\n",
    "\n",
    "from sklearn.metrics import euclidean_distances\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from collections import defaultdict\n",
    "from sklearn.metrics.pairwise import cosine_distances\n",
    "from scipy.stats import spearmanr\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import scanpy as sc\n",
    "from sklearn.metrics import adjusted_mutual_info_score, rand_score\n",
    "from collections import Counter\n",
    "from scipy.stats import mode\n",
    "import operator\n",
    "\n",
    "# --------\n",
    "\n",
    "class cross_species_acc():\n",
    "    \"\"\"\n",
    "    Class for calculating cross species accuracy metrics\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, adata, base_species='human', \n",
    "                 target_species='mouse', label_col='CL_class_coarse', \n",
    "                 metric='cosine', medoid=False, space='raw'):\n",
    "\n",
    "        self.adata = adata\n",
    "        self.base_species = base_species\n",
    "        self.target_species = target_species\n",
    "        self.label_col = label_col\n",
    "        self.metric = metric\n",
    "        self.medoid=medoid\n",
    "        self.space=space\n",
    "        \n",
    "        # Calculate accuracy metrics\n",
    "        self.calc_cross_species_label_matches()\n",
    "\n",
    "    def find_all_species_centres(self):\n",
    "        \"\"\"\n",
    "        Finds all species-specific centroids given an AnnData object\n",
    "        \"\"\"\n",
    "        if self.space == 'umap':\n",
    "            key = 'X_umap'\n",
    "        elif self.space == 'samap':\n",
    "            key = 'X_umap_samap'\n",
    "        elif self.space == 'scanorama':\n",
    "            key = 'X_scanorama'\n",
    "        elif self.space == 'harmony':\n",
    "            key = 'X_harmony'\n",
    "        \n",
    "        centres = {}\n",
    "        centres['size'] = {}\n",
    "        base_cluster_sizes = {}\n",
    "        for species in self.adata.obs['species'].unique():\n",
    "            centres[species] = {}\n",
    "            species_set = self.adata[self.adata.obs['species']==species]\n",
    "            \n",
    "            for l in species_set.obs[self.label_col].unique():\n",
    "                subset = species_set[species_set.obs[self.label_col] == l]\n",
    "                \n",
    "                # If space is not raw then use the right obsm column\n",
    "                if self.space != 'raw':\n",
    "                    subset_data = subset.obsm[key]\n",
    "                else:\n",
    "                    subset_data = subset.X.toarray()\n",
    "\n",
    "                # Deal with exceptions\n",
    "                if len(subset)<1:\n",
    "                    continue\n",
    "                elif len(subset)==1:\n",
    "                    centres[species][l] = subset_data[0]\n",
    "                \n",
    "                # Use centroid or medoid\n",
    "                centroid = np.mean(subset_data, 0)\n",
    "                if self.medoid:\n",
    "                    centres[species][l] =\\\n",
    "                        self.get_medoid(subset_data, centroid)\n",
    "                else:\n",
    "                    centres[species][l] = centroid\n",
    "                    \n",
    "                # This is for normalization of distances\n",
    "                if species == self.base_species:\n",
    "                    dist_mat = euclidean_distances(subset_data)\n",
    "                    centres['size'][l] = np.max(dist_mat)\n",
    "            \n",
    "        return pd.DataFrame(centres).dropna()\n",
    "\n",
    "    \n",
    "    def calc_cross_species_label_matches(self):\n",
    "        \"\"\"\n",
    "        Given Anndata object, returns:\n",
    "        - matches: number of cluster centres in base species that \n",
    "        have the same cluster label in the target species as nn\n",
    "        - dist: 'normalized' distance between cluster centre of base \n",
    "        species and the cluster centre with the same label in target \n",
    "        species\n",
    "        \n",
    "        TODO: This is not generalized to more than 2 species\n",
    "        \"\"\"\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        \n",
    "        centres = self.find_all_species_centres()\n",
    "        dist = 0\n",
    "        norm_dist = 0\n",
    "        matches = 0\n",
    "        matches_names = []\n",
    "        matches_names_all = []\n",
    "        target_centres = np.vstack(\n",
    "                   centres.loc[:,self.target_species].values)\n",
    "        \n",
    "        for idx, ctype in enumerate(centres.index):\n",
    "            base = centres.loc[ctype, self.base_species]\n",
    "            base_targets = np.vstack([base, target_centres])\n",
    "\n",
    "            if self.metric=='cosine':\n",
    "                distances = cosine_distances(base_targets)[0][1:]\n",
    "                \n",
    "            pred_match = np.argmin(distances)\n",
    "            if  pred_match == idx:\n",
    "                matches += 1\n",
    "                matches_names.append(ctype)\n",
    "            \n",
    "            matches_names_all.append((ctype,\n",
    "                         centres.index[pred_match]))\n",
    "            dist += distances[idx]\n",
    "            norm_dist += distances[idx]/centres.loc[ctype, 'size']\n",
    "\n",
    "        self.cross_species_label_dist = dist\n",
    "        self.cross_species_label_norm_dist = norm_dist\n",
    "        self.cross_species_label_matches = matches\n",
    "        self.cross_species_label_matches_names = matches_names\n",
    "        self.cross_species_label_matches_names_all = matches_names_all\n",
    "        \n",
    "        warnings.filterwarnings(\"always\")\n",
    "     \n",
    "    \n",
    "    def get_medoid(self, data, centroid):\n",
    "        dists = euclidean_distances(np.vstack([centroid,data]))[0]\n",
    "        return data[np.argsort(dists)[1]-1]\n",
    "        \n",
    "        \n",
    "# --------\n",
    "\n",
    "\n",
    "class embedding_CL_comparison():\n",
    "    \"\"\"\n",
    "    Class for comparing embedding with cell ontology\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, adata, label_col='CL_class_coarse', CL_ID_col='CL_ID_coarse',\n",
    "                 metric='cosine', features='raw'):\n",
    "\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        self.adata = adata\n",
    "        self.label_col = label_col\n",
    "        self.CL_ID_col = CL_ID_col\n",
    "        self.metric = metric\n",
    "        self.features = features\n",
    "        self.labels = self.adata.obs[self.label_col].unique()\n",
    "        self.centres = []\n",
    "        self.centres_ranked = []\n",
    "        self.CL_centres_ranked = []\n",
    "\n",
    "        # Get centres, nns and ranks\n",
    "        self.get_centre_ranks()\n",
    "        self.get_CL_ranks()\n",
    "\n",
    "        # Calculate metrics\n",
    "        self.spearman_corr = {}\n",
    "        self.hits_at_k = {}\n",
    "        for id_ in self.labels:         \n",
    "            self.spearman_corr[id_] = spearmanr(self.CL_centres_ranked[id_], \n",
    "                self.centres_ranked[id_])[0]\n",
    "            self.hits_at_k[id_] = self.get_hits_topk(self.CL_centres_ranked[id_], \n",
    "                self.centres_ranked[id_])\n",
    "            \n",
    "        warnings.filterwarnings(\"always\")\n",
    "\n",
    "        \n",
    "    # Implement cluster centroid\n",
    "\n",
    "    def find_centre(self, cluster, medioid=False):\n",
    "        \"\"\"\n",
    "        Find cluster centre: either centroid or medioid\n",
    "        \"\"\"\n",
    "        if medioid:\n",
    "            dist = euclidean_distances(cluster)\n",
    "            medioid = np.argmin(dist.sum(0))\n",
    "            return cluster[medioid].toarray()\n",
    "\n",
    "        else:\n",
    "            return np.mean(cluster,0)\n",
    "    \n",
    "    \n",
    "    def get_outlier_idx(self, CL_centres, centres, k=10):\n",
    "        \"\"\"\n",
    "        Get top or bottom ranked nn\n",
    "        \"\"\"\n",
    "        outliers = []\n",
    "        for i, pair in enumerate(list(zip(CL_centres, centres))):\n",
    "            if pair[0] < k or pair[1] < k:\n",
    "                outliers.append(i)\n",
    "        return outliers\n",
    "\n",
    "    \n",
    "    def get_hits_topk(self, CL_centres, centres, k=10):\n",
    "        \"\"\"\n",
    "        Get numbers of matches within top k\n",
    "        \"\"\"\n",
    "        return len(set(CL_centres[:k]).intersection(set(centres[:k])))\n",
    "        \n",
    "\n",
    "    def get_centre_ranks(self):\n",
    "        \"\"\"\n",
    "        Get nn ranks for cluster centres\n",
    "        \"\"\"\n",
    "        for cell_type in self.labels:\n",
    "            if self.features=='raw':\n",
    "                self.centres.append(self.find_centre(\n",
    "                    self.adata[self.adata.obs[self.label_col] == cell_type].X))\n",
    "        self.centres = np.vstack(self.centres)\n",
    "                \n",
    "        if self.metric=='euclidean':\n",
    "            centres_dist = euclidean_distances(self.centres)\n",
    "\n",
    "        if self.metric=='cosine':\n",
    "            centres_dist = cosine_distances(self.centres)\n",
    "\n",
    "        self.centres_ranked = {k:v for k,v in zip(\n",
    "            self.labels, np.argsort(centres_dist))}\n",
    "        \n",
    "    def get_CL_ranks(self):\n",
    "        \"\"\"\n",
    "        Get nn ranks for cell ontology cluster centres\n",
    "        \"\"\"\n",
    "        all_CL_distances = pd.read_csv('/dfs/project/cross-species/data/lung/shared/CL_similarity_RW.csv',\n",
    "                                       index_col=0)\n",
    "\n",
    "        CL_sim_matrix = all_CL_distances.loc[self.adata.obs[self.CL_ID_col].unique(),\n",
    "                                             self.adata.obs[self.CL_ID_col].unique()]\n",
    "        \n",
    "        ID_dict = self.adata.obs.set_index(self.label_col).to_dict()['CL_ID_coarse']\n",
    "        inv_ID_dict = {v: k for k, v in ID_dict.items()}\n",
    "\n",
    "        self.CL_centres_ranked = {inv_ID_dict[k]:v for k,v in zip(self.adata.obs['CL_ID_coarse'].unique(),\n",
    "                                                           np.argsort(-CL_sim_matrix.values))}\n",
    "\n",
    "    \n",
    "    def plot_rank_scatter(self):\n",
    "        \"\"\"\n",
    "        Create rank scatter plot between embedding nn and CL nn\n",
    "        \"\"\"\n",
    "        fig, axs = plt.subplots(5, 5, sharex=True, sharey=True, figsize=[20,15])\n",
    "        it = 0\n",
    "        spearman_corr = {}\n",
    "        hits_at_k = {}\n",
    "        outlier=False\n",
    "\n",
    "        for i in range(5):\n",
    "            for j in range(5):\n",
    "                if it == len(self.labels):\n",
    "                    break\n",
    "\n",
    "                id_ = self.labels[it]\n",
    "                if outlier:\n",
    "                    outlier_idx = get_outlier_idx(self.CL_centres_ranked[id_], \n",
    "                        self.centres_ranked[id_])\n",
    "                    axs[i, j].scatter(self.CL_centres_ranked[id_][outlier_idx], \n",
    "                    self.centres_ranked[id_][outlier_idx])\n",
    "                else:\n",
    "                    axs[i, j].scatter(self.CL_centres_ranked[id_], \n",
    "                    self.centres_ranked[id_])\n",
    "                axs[i, j].set_title(id_)\n",
    "                axs[i, j].plot([0,32],[0,32], 'k')   \n",
    "                it += 1\n",
    "                \n",
    "    def plot_hits_at_k(self):\n",
    "        plot_df = pd.DataFrame.from_dict(self.hits_at_k, orient='index')\n",
    "        plot_df = plot_df.rename(columns={0:'Value'})\n",
    "        plot_df = plot_df.sort_values('Value')\n",
    "\n",
    "        plt.figure(figsize=[8,12])\n",
    "        plt.barh(plot_df.index, plot_df['Value'])\n",
    "        plt.ylabel('Cell Type')\n",
    "        plt.xlabel('Hits @ k')\n",
    "        plt.title('Hits @ k (Embedding space compared to Cell Ontology)')\n",
    "        plt.xlim([0,10])\n",
    "        \n",
    "        \n",
    "    def plot_spearman(self):\n",
    "        plot_df = pd.DataFrame.from_dict(self.spearman_corr, orient='index')\n",
    "        plot_df = plot_df.rename(columns={0:'Value'})\n",
    "        plot_df = plot_df.sort_values('Value')\n",
    "\n",
    "        plt.figure(figsize=[8,12])\n",
    "        plt.barh(plot_df.index, plot_df['Value'])\n",
    "        plt.ylabel('Cell Type')\n",
    "        plt.xlabel('Spearman Correlation')\n",
    "        plt.title('Spearman Correlation (Embedding space compared to Cell Ontology)')\n",
    "        plt.xlim([-1,1])\n",
    "          \n",
    "        \n",
    "# --------\n",
    "\n",
    "## KNN analysis per cell\n",
    "## TODO integrate these functions into cross_species_acc class\n",
    "\n",
    "def get_knn_label(cell_names, adata, col):\n",
    "    \"\"\"\n",
    "    Returns majority class labels of nearest neighbors. \n",
    "    Will return random label in case of tie\n",
    "    \"\"\"\n",
    "    \n",
    "    return adata[cell_names].obs[col].value_counts().index[0]\n",
    "\n",
    "\n",
    "def cross_species_knn_all(adata, k=1, species='human', space='raw',\n",
    "                          col = 'cell_type', metric='euclidean',\n",
    "                          verbose = False, consider_same_species=False):\n",
    "    \"\"\"Runs cross species k nearest neighbor on all cells\n",
    "    \"\"\"\n",
    "\n",
    "    # Create distance matrix\n",
    "    if space == 'raw':\n",
    "        X = adata.X\n",
    "    elif space == 'umap':\n",
    "        X = adata.obsm['X_umap']\n",
    "    elif space == 'samap':\n",
    "        X = adata.obsm['X_umap_samap']\n",
    "    elif space == 'scanorama':\n",
    "        X = adata.obsm['X_scanorama']\n",
    "    elif space == 'harmony':\n",
    "        X = adata.obsm['X_harmony']\n",
    "    \n",
    "    # Slow step\n",
    "    if metric == 'euclidean':\n",
    "        dist_mat = euclidean_distances(X)\n",
    "    elif metric == 'cosine':\n",
    "        dist_mat = cosine_distances(X)\n",
    "        \n",
    "    if consider_same_species:\n",
    "        # Get indices for species and nonspecies cells\n",
    "        species_idx = np.where(adata.obs['species']==species)[0]\n",
    "        adata.obs['temp_label'] = adata.obs['species'].astype(str) +\\\n",
    "                            '_' + adata.obs[col].astype(str)\n",
    "\n",
    "        nns = []\n",
    "        for idx in species_idx:\n",
    "            curr_temp_label = adata.obs['temp_label'][idx]\n",
    "            row = dist_mat[idx,:]\n",
    "            possible_nbrs = np.where(adata.obs['temp_label'] != curr_temp_label)[0]\n",
    "            \n",
    "            row = row[possible_nbrs]\n",
    "            nns.append(possible_nbrs[np.argpartition(row, k)[:k]])\n",
    "            \n",
    "        nbrs = [(adata.obs[col][x], Mode(adata.obs[col][y].astype('str').values)) \n",
    "                        for x,y in zip(species_idx, nns)]\n",
    "    \n",
    "    else:\n",
    "        # Get indices for species and nonspecies cells\n",
    "        species_idx = np.where(adata.obs['species']==species)[0]\n",
    "        nonspecies_idx = np.where(adata.obs['species'] != species)[0]\n",
    "\n",
    "        # Slow step\n",
    "        reduced_dist_mat = dist_mat[species_idx,:][:,nonspecies_idx]\n",
    "        \n",
    "        nns = [list(nonspecies_idx[y]) \n",
    "               for y in np.argpartition(reduced_dist_mat, k)[:,:k]]\n",
    "\n",
    "        nbrs = [(adata.obs[col][x], Mode(adata.obs[col][y].astype('str').values)) \n",
    "                        for x,y in zip(species_idx, nns)]\n",
    "    \n",
    "    return nbrs\n",
    "\n",
    "\n",
    "def cluster_knn(cluster_knn_df, label):\n",
    "    \"\"\"\n",
    "    Given majority k nearest cross species neigbhor class for each cell, \n",
    "    identifies the k nearest neighbors for the given cluster\n",
    "    \"\"\"\n",
    "    \n",
    "    list_ = list(cluster_knn_df[cluster_knn_df['Source_Cell']==label].\n",
    "                value_counts().items())\n",
    "    x = pd.DataFrame([(s[0][1],s[1]) for s in list_])\n",
    "    x['Source_Cluster'] = list_[0][0][0]\n",
    "    x = x.rename(columns={0:'Cross_Species_KNN_Label', 1:'Score'})\n",
    "    x['Score'] = x['Score']/x['Score'].sum()\n",
    "    return x\n",
    "\n",
    "\n",
    "def cluster_knn_all(all_nbrs):\n",
    "    \"\"\"\n",
    "    Given majority k nearest cross species neigbhor class for each cell, \n",
    "    identifies the k nearest neighbors for all clusters\n",
    "    \"\"\"\n",
    "    cluster_knn_df = pd.DataFrame(all_nbrs)\n",
    "    cluster_knn_df = cluster_knn_df.rename(columns={0:'Source_Cell',1:'Cross_Species_KNN'})\n",
    "\n",
    "    return [cluster_knn(cluster_knn_df, c) \n",
    "         for c in cluster_knn_df['Source_Cell'].unique()]   \n",
    "\n",
    "\n",
    "def plot_cluster_knn_bar(df, source='human', other='mouse', ax=None, title=None):\n",
    "    \"\"\"\n",
    "    Creates a stacked bar plot to identify majority k nearest neighbors for\n",
    "    a given cluster, on a specific axis\n",
    "    \"\"\"\n",
    "    if ax is None:\n",
    "        fig, (ax) = plt.subplots(1, 1, sharex=True, sharey=True, figsize=(6, 17), frameon=False)\n",
    "    bars = defaultdict(int)\n",
    "    colors = defaultdict(int)\n",
    "    tick = -1; \n",
    "    tick_pos = {}\n",
    "    colors_list = ['','r','g','b','y','k','purple','g','b','y','k','g','b','y',\n",
    "                                                       'k','g','b','y','k',\n",
    "                                                       'k','g','b','y','k',\n",
    "                                                       'k','g','b','y','k'] + [\"w\"]*50\n",
    "    df = df.sort_values(['Source_Cluster', 'Score'], ascending=False)\n",
    "\n",
    "    for i in df.iterrows():\n",
    "        color = 'k'\n",
    "        x = i[1]['Source_Cluster']\n",
    "        y = i[1]['Score']        \n",
    "\n",
    "        if i[1]['Cross_Species_KNN_Label'] == i[1]['Source_Cluster']:\n",
    "            color= 'w'\n",
    "        left = bars[x]\n",
    "        bars[x] = bars[x] + y\n",
    "        colors[x] = colors[x] + 1\n",
    "        if colors_list[colors[x]] == 'r':    \n",
    "            tick = tick+1\n",
    "            tick_pos[x] = tick\n",
    "            if bars[x]>=0.25:\n",
    "                ax.text(0.1,tick-0.2,i[1]['Cross_Species_KNN_Label'], color=color)\n",
    "        ax.barh(tick_pos[x], y, left=left, color=colors_list[colors[x]], alpha=0.5)\n",
    "        \n",
    "    keys = sorted(tick_pos.keys())\n",
    "    vals = [tick_pos[k] for k in keys]\n",
    "    ax.set_yticks(vals, keys)\n",
    "    ax.set_ylabel(source)\n",
    "    ax.set_xlabel('Percentage of cells with cross-species KNN class')\n",
    "    if title is not None:\n",
    "        ax.set_title(title)\n",
    "    \n",
    "    return \n",
    "\n",
    "## ---------------------------------\n",
    "## Alignment scores\n",
    "## -----------------------------------\n",
    "\n",
    "def alignment_score(fname, col='cell_type', space='raw', k=1,\n",
    "                    species='human', consider_same_species=False):\n",
    "    adata = sc.read_h5ad(fname)\n",
    "    if space=='umap':\n",
    "        sc.pp.pca(adata, n_comps=50)\n",
    "        sc.pp.neighbors(adata, n_neighbors=15)\n",
    "        sc.tl.umap(adata)\n",
    "    all_nbrs = cross_species_knn_all(adata, col=col, metric='cosine', space=space, k=k,\n",
    "                                     species=species, consider_same_species=consider_same_species)\n",
    "    all_cluster_nbrs  = [i for i in cluster_knn_all(all_nbrs)]\n",
    "    all_cluster_nbrs = pd.concat(all_cluster_nbrs).reset_index(drop=True) \n",
    "    return all_cluster_nbrs\n",
    "\n",
    "def compare_matches(alignments, true_alignments):\n",
    "    aligns = alignments.merge(true_alignments, on=['Source_Cluster', 'Cross_Species_KNN_Label'])\n",
    "    #aligns = aligns.merge(true_alignments, on=['Source_Cluster'], how='outer').fillna(0)\n",
    "    \n",
    "    return aligns\n",
    "\n",
    "def score_matches(alignments, true_alignments, thresh=0.5, ret_matches=False):\n",
    "    matches = compare_matches(alignments, true_alignments)\n",
    "    if ret_matches:\n",
    "        return matches[matches['Score']>thresh]\n",
    "    else:\n",
    "        return sum(matches['Score']>thresh)\n",
    "    return\n",
    "\n",
    "def create_comparison_plot_df(all_cluster_nbrs1, all_cluster_nbrs2, true_map):\n",
    "    \n",
    "    knn_scores_1 = compare_matches(all_cluster_nbrs1, true_map)\n",
    "    knn_scores_2 = compare_matches(all_cluster_nbrs2, true_map)\n",
    "\n",
    "    plot_df = knn_scores_1.merge(knn_scores_2, on='Source_Cluster') \n",
    "    return plot_df\n",
    "\n",
    "def get_comparison_plot(plot_df, bars=2, labels_=['Method1', 'Method2']):\n",
    "    plt.figure(figsize=[20,5])\n",
    "    ax=plt.gca()\n",
    "\n",
    "    labels = plot_df['Source_Cluster'].values\n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.35  # the width of the bars\n",
    "\n",
    "    #fig, ax = plt.subplots()\n",
    "    rects1 = ax.bar(x - width/2, plot_df['Score_x'].values, width, label=labels_[0])\n",
    "    if bars == 2:\n",
    "        rects2 = ax.bar(x + width/2, plot_df['Score_y'].values, width, label=labels_[1])\n",
    "\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    ax.set_ylabel('% of nearest neighbors of\\n correct cross species label')\n",
    "    ax.set_title('Cross species cell type alignment')\n",
    "    plt.xticks(x, labels, rotation='vertical')\n",
    "    ax.set_xticklabels(labels)\n",
    "    ax.legend() \n",
    "\n",
    "    \n",
    "def get_cell_alignment(align_df, true_df, count_df):\n",
    "    align_df['Source_Cluster'] = align_df['Source_Cluster'].astype('str')\n",
    "    count_df['Source_Cluster'] = count_df['Source_Cluster'].astype('str')\n",
    "    true_df['Source_Cluster'] = true_df['Source_Cluster'].astype('str')\n",
    "    align_df['Cross_Species_KNN_Label'] = align_df['Cross_Species_KNN_Label'].astype('str')                  \n",
    "    true_df['Cross_Species_KNN_Label'] = true_df['Cross_Species_KNN_Label'].astype('str')\n",
    "    \n",
    "    df = align_df.merge(true_df, on=['Source_Cluster', 'Cross_Species_KNN_Label'])\n",
    "    df = df.merge(count_df, on='Source_Cluster', how='outer').fillna(0)\n",
    "    return sum((df['Score']*df['count'])/sum(df['count']))\n",
    "\n",
    "\n",
    "def get_alignment_metrics(fname, out_label = 'labels2', orig_label='CL_class_coarse',\n",
    "                          space='raw', species=['human','mouse'], k=1,\n",
    "                 true_labels_path=None, ret_matches = False, consider_same_species=False):\n",
    "    \"\"\"\n",
    "    Function for computing evaluation metrics for embedding\n",
    "    \n",
    "    Outputs:\n",
    "    - species1_nn: Number of cross-species label matches (species 1)\n",
    "    - species2_nn: Number of cross-species label matches (species 2)\n",
    "    - union_nn: Number of cross-species label matches in either species \n",
    "    - mutual_nn: Number of cross-species label matches in both species \n",
    "    - cell_score1: Percentage of cells in species 1 with cross species nn of correct label\n",
    "    - cell_score2: Percentage of cells in species 2 with cross species nn of correct label\n",
    "    - cell_score_combine: Percentage of cells in both species with cross species nn of correct label\n",
    "    - centroid_matches_species1: Number of species 1 centroids that are nn with correct species 2 centroid\n",
    "    - centroid_matches_species2: Number of species 2 centroids that are nn with correct species 1 centroid\n",
    "    - centroid_matches_union: Union of centroid lists\n",
    "    - medoid_matches_species1: Number of species 1 medoids that are nn with correct species 2 medoid\n",
    "    - medoid_matches_species2: Number of species 2 medoids that are nn with correct species 1 medoid\n",
    "    - medoid_matches_union: Union of medoid lists\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: This is a very ugly function that needs to be made into a class alongwith\n",
    "    # the functions above it\n",
    "        \n",
    "    # Get cross-species only alignments\n",
    "    alignments = []\n",
    "    print('Finding nns for species 1')\n",
    "    alignments.append(alignment_score(fname, out_label, space=space, species=species[0], k=k,\n",
    "                                     consider_same_species=consider_same_species))\n",
    "    print('Finding nns for species 2')\n",
    "    alignments.append(alignment_score(fname, out_label, space=space, species=species[1], k=k,\n",
    "                                     consider_same_species=consider_same_species))\n",
    "    \n",
    "    # Get true labels\n",
    "    if true_labels_path is None:\n",
    "        if orig_label == 'CL_class_coarse':\n",
    "            true_labels_path = '/dfs/project/cross-species/data/lung/shared/true_CL_class_coarse.csv'\n",
    "        elif orig_label == 'cell_type':\n",
    "            true_labels_path = '/dfs/project/cross-species/data/lung/shared/true_cell_type.csv'\n",
    "        else:\n",
    "            print(\"ERROR: True labels unavailable for this column!, Please set manually\")\n",
    "            return\n",
    "    \n",
    "    true_labels = pd.read_csv(true_labels_path, index_col=0)\n",
    "    cols = []\n",
    "    results = {}\n",
    "    cols.append([c for c in true_labels.columns if species[0] in c][0])\n",
    "    cols.append([c for c in true_labels.columns if species[1] in c][0])\n",
    "\n",
    "    true_dfs = []\n",
    "    true_dfs.append(true_labels.rename(columns={\n",
    "        cols[0]:'Source_Cluster', cols[1]:'Cross_Species_KNN_Label'}))\n",
    "    true_dfs.append(true_labels.rename(columns={\n",
    "        cols[1]:'Source_Cluster', cols[0]:'Cross_Species_KNN_Label'}))\n",
    "\n",
    "    # Score matches\n",
    "    matches = []\n",
    "    matches.append(score_matches(alignments[0], \n",
    "                    true_dfs[0], thresh=0.5, ret_matches=True))\n",
    "    matches.append(score_matches(alignments[1], \n",
    "                    true_dfs[1], thresh=0.5, ret_matches=True))\n",
    "        \n",
    "    for m in matches:\n",
    "        m = m.rename(columns = {'Cross_Species_KNN_Label_x':'Cross_Species_KNN_Label'})\n",
    "        m = m.loc[:,['Score', 'Source_Cluster', 'Cross_Species_KNN_Label']]\n",
    "    results['species1_nn'] = len(matches[0])\n",
    "    results['species2_nn'] = len(matches[1])\n",
    "\n",
    "    # Combine matches\n",
    "    all_matches = matches[0].merge(matches[1], \n",
    "                         left_on=['Source_Cluster', 'Cross_Species_KNN_Label'],\n",
    "                         right_on=['Cross_Species_KNN_Label', 'Source_Cluster'], how='outer')\n",
    "    results['union_nn'] = len(all_matches)\n",
    "    results['mutual_nn'] = len(matches[0].merge(matches[1], \n",
    "                         left_on=['Source_Cluster', 'Cross_Species_KNN_Label'],\n",
    "                         right_on=['Cross_Species_KNN_Label', 'Source_Cluster'], how='inner'))\n",
    "\n",
    "    # Get per-cell alignment scores\n",
    "    adata = sc.read_h5ad(fname)\n",
    "    adata = adata[adata.obs['species'].isin(species)]\n",
    "    ratio1 = sum(adata.obs['species']==species[0])/len(adata)\n",
    "    ratio2 = sum(adata.obs['species']==species[1])/len(adata)\n",
    "\n",
    "    adata1 = adata[adata.obs['species']==species[0]]\n",
    "    adata2 = adata[adata.obs['species']==species[1]]\n",
    "    count_dfs = []\n",
    "    count_dfs.append(pd.DataFrame(adata1.obs[out_label].value_counts()).reset_index().rename(\n",
    "                    columns={'index':'Source_Cluster', out_label:'count'}))\n",
    "    count_dfs.append(pd.DataFrame(adata2.obs[out_label].value_counts()).reset_index().rename(\n",
    "                    columns={'index':'Source_Cluster', out_label:'count'}))\n",
    "\n",
    "    cell_scores = []\n",
    "    cell_scores.append(get_cell_alignment(alignments[0], true_dfs[0], count_dfs[0]))\n",
    "    cell_scores.append(get_cell_alignment(alignments[1], true_dfs[1], count_dfs[1]))\n",
    "    results['cell_score1'] = cell_scores[0]\n",
    "    results['cell_score2'] = cell_scores[1]\n",
    "    results['cell_score_combine'] = ratio1*cell_scores[0] + ratio2*cell_scores[1]\n",
    "\n",
    "    # Get centroid nn score:\n",
    "    for centre,flag in [('centroid', False), ('medoid', True)]:\n",
    "        c_nn1 = cross_species_acc(adata, base_species=species[0], target_species=species[1], \n",
    "                          label_col=out_label, medoid=flag, space=space)\n",
    "        c_nn2 = cross_species_acc(adata, base_species=species[1], target_species=species[0], \n",
    "                          label_col=out_label, medoid=flag, space=space)\n",
    "       \n",
    "    \n",
    "        results[centre+'_matches_species1'] = c_nn1.cross_species_label_matches\n",
    "        results[centre+'_matches_species2'] = c_nn2.cross_species_label_matches\n",
    "        results[centre+'_matches_union'] = len(set(c_nn1.cross_species_label_matches_names).union(\n",
    "                                            set(c_nn2.cross_species_label_matches_names)))\n",
    "    \n",
    "    if ret_matches == True:\n",
    "        return (results, matches, alignments)\n",
    "    \n",
    "    else:\n",
    "        return results\n",
    "    \n",
    "    \n",
    "def get_louvain_metrics(fname, label='cell_type'):\n",
    "    # Compute adjusted rand index for measuring label alignment across species using ground truth information\n",
    "    \n",
    "    adata = sc.read_h5ad(fname)\n",
    "    \n",
    "    try:\n",
    "        sc.pp.pca(adata, n_comps=50)\n",
    "        sc.pp.neighbors(adata, n_neighbors=15)\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    if '_' not in adata.obs[label].values[0]:\n",
    "        adata.obs[label].str.cat(adata.obs[\"species\"], sep=\"_\")\n",
    "    \n",
    "    metrics = {}\n",
    "    for resolution in [10, 5, 2, 1, 0.8, 0.5, 0.4, 0.2, 0.1, 0.01, 0.001, 0.0001]:\n",
    "        print('Calculating for resolution: ', str(resolution))\n",
    "        sc.tl.louvain(adata, resolution=resolution)\n",
    "\n",
    "        true_clusters = pd.read_csv('/dfs/project/cross-species/data/lung/shared/true_cell_type_clusters.csv', index_col=0)\n",
    "        true_clusters = true_clusters.merge(adata.obs, left_on='cell_type', right_on=label)\n",
    "        \n",
    "        metrics[resolution] = {\n",
    "            #'ARI':adjusted_rand_score(true_clusters['cluster'], true_clusters['louvain'].astype('int')),\n",
    "            'RI':rand_score(true_clusters['cluster'], true_clusters['louvain'].astype('int')),\n",
    "            'AMI': adjusted_mutual_info_score(true_clusters['cluster'], true_clusters['louvain'].astype('int'))\n",
    "        }\n",
    "\n",
    "    return metrics\n",
    "\n",
    "\n",
    "# Maria's cell type reannotation function\n",
    "def reannotate(adata, source='human', target='mouse', label='cell_type'):\n",
    "    for resolution in [2, 1, 0.8, 0.6, 0.4, 0.2, 0.1]:\n",
    "        sc.tl.louvain(adata, resolution)\n",
    "        louvain_clusters = set(adata.obs['louvain'])\n",
    "\n",
    "        reannotated = {}\n",
    "        for c in louvain_clusters:\n",
    "            current_cluster = adata[adata.obs['louvain']==c]\n",
    "            if len(set(current_cluster.obs['species']))==2:\n",
    "                cluster_source = current_cluster[current_cluster.obs['species']==source]\n",
    "                cluster_target = current_cluster[current_cluster.obs['species']==target]\n",
    "                c = Counter(cluster_source.obs[label])\n",
    "                major_cell_type = max(c.items(), key=operator.itemgetter(1))[0]      \n",
    "                for c in cluster_target.obs_names:\n",
    "                    if c not in reannotated:\n",
    "                        reannotated[c] = major_cell_type\n",
    "    adata_source = adata[adata.obs['species']==source]\n",
    "    tmp = dict(zip(adata_source.obs_names, adata_source.obs[label]))\n",
    "    reannotated = {**reannotated, **tmp}\n",
    "    adata.obs['reannotated_'+source] = [reannotated[c] if c in reannotated else 'None' \n",
    "                                  for c in adata.obs_names]\n",
    "    \n",
    "def get_reannotation_metrics(fname, label='cell_type', source='human', target='mouse'):\n",
    "    # This is current specific to mouse reannotation\n",
    "    \n",
    "    adata = sc.read_h5ad(fname)\n",
    "    sc.pp.neighbors(adata)\n",
    "    \n",
    "    if '_' in adata.obs[label].values[0]:\n",
    "        label = 'labels2'\n",
    "    \n",
    "    reannotate(adata, source=source, label=label)\n",
    "    m = adata[adata.obs['species']==target]\n",
    "    \n",
    "    true_labels_path = '/dfs/project/cross-species/data/lung/shared/true_cell_type.csv'\n",
    "    true_labels = pd.read_csv(true_labels_path, index_col=0)\n",
    "    results_df = true_labels.merge(m.obs, left_on=source+'_cell_type', right_on='reannotated_'+source)\n",
    "    return np.mean(results_df[target+'_cell_type'] == results_df[label])\n",
    "\n",
    "## ---------------------------------\n",
    "## General helper functions\n",
    "## -----------------------------------\n",
    "\n",
    "def plotly_scatter(adata, embed = 'X_umap', label= 'cell_type', \n",
    "                   hover_cols = ['cell_type', 'species']):\n",
    "    plot_df = pd.DataFrame(adata.obsm[embed])\n",
    "    plot_df[label] = adata.obs[label].values\n",
    "    for c in hover_cols:\n",
    "        plot_df[c] = adata.obs[c].values\n",
    "    \n",
    "    fig = px.scatter(plot_df, x=0, y=1, \n",
    "                     hover_name=label,\n",
    "                     color = label,\n",
    "                     hover_data=hover_cols)\n",
    "\n",
    "    fig.show()\n",
    "    \n",
    "def Mode(arr):\n",
    "    # Wrapper for mode\n",
    "    return mode(arr)[0][0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d4a0d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "map_labels = {\n",
    "    \"B cell\":\"b cell\",\n",
    "    \"BCell\":\"b cell\",\n",
    "    \"Beam A\":\"beam\",\n",
    "    \"Beam X\":\"beam\",\n",
    "    \"Beam Y\":\"beam\",\n",
    "    \"BeamA\":\"beam\",\n",
    "    \"BeamCella\":\"beam\",\n",
    "    \"BeamCellb\":\"beam\",\n",
    "    \"Ciliary muscle\":\"Ciliary muscle\",\n",
    "    \"CiliaryMuscle\":\"Ciliary muscle\",\n",
    "    \"Collector channel\":\"Collector channel\",\n",
    "    \"CollectorChnlAqVein\":\"Collector channel\",\n",
    "    \"Corneal\":\"Corneal\",\n",
    "    \"Corneal endothelium\":\"Corneal\",\n",
    "    \"Corneal epithelium\":\"Corneal\",\n",
    "    \"CornealEpi\":\"Corneal\",\n",
    "    \"CribiformJCT\":\"JCT\",\n",
    "    \"Endothelium\":\"Endothelium\",\n",
    "    \"Fibroblast\":\"Fibroblast\",\n",
    "    \"JCT\":\"JCT\",\n",
    "    \"Macrophage\":\"Macrophage\",\n",
    "    \"MastCell\":\"Mast\",\n",
    "    \"Melanocyte\":\"Melanocyte\",\n",
    "    \"Myelinating schwann cell\":\"Schwann\",\n",
    "    \"Myoepithelium\":\"Myoepithelium\",\n",
    "    \"NK/T\":\"NKT\",\n",
    "    \"NK/T cell\":\"NKT\",\n",
    "    \"NKT\":\"NKT\",\n",
    "    \"Neuron\":\"Neuron\",\n",
    "    \"Nonmyelinating schwann cell\":\"Schwann\",\n",
    "    \"Nonpigmented ciliary epithelium\":\"Ciliary epithelium\",\n",
    "    \"Pericyte\":\"Pericyte\",\n",
    "    \"Pigmented ciliary epithelium\":\"Ciliary epithelium\",\n",
    "    \"Pigmented epithelium\":\"Ciliary epithelium\",\n",
    "    \"SChlemm's Canal\":\"SC\",\n",
    "    \"ScEndo\":\"SC\",\n",
    "    \"SchwalbeLine\":\"Schwalbe\",\n",
    "    \"Schwann cell\":\"Schwann\",\n",
    "    \"SchwannCell-my\":\"Schwann\",\n",
    "    \"SchwannCell-nmy\":\"Schwann\",\n",
    "    \"Uveal\":\"Uveal\",\n",
    "    \"Vascular\":\"Vascular\",\n",
    "    \"Vascular Endothelium\":\"Vascular\",\n",
    "    \"VascularEndo\":\"Vascular\",\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7862c3de",
   "metadata": {},
   "outputs": [],
   "source": [
    "h5s = [a for a in glob.glob(\"/dfs/project/cross-species/yanay/data/centroids_seeds/metric_results/*glaucoma_run_*ESM2*h5ad\") if \"pretrain\" not in a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "05644c94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Macrophage', 'SchwannCell-nmy', 'Pericyte', 'BeamCella', 'CiliaryMuscle', ..., 'Pigmented epithelium', 'Beam Y', 'Myoepithelium', 'Vascular Endothelium', 'Corneal']\n",
       "Length: 44\n",
       "Categories (44, object): ['B cell', 'BCell', 'Beam A', 'Beam X', ..., 'Uveal', 'Vascular', 'Vascular Endothelium', 'VascularEndo']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad.obs[\"labels2\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a95a8d2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Your filename has more than two extensions: ['.0_pe_1', '.0_ESM2_seed_29', '.h5ad'].\n",
      "Only considering the two last: ['.0_ESM2_seed_29', '.h5ad'].\n",
      "WARNING: Your filename has more than two extensions: ['.0_pe_1', '.0_ESM2_seed_29', '.h5ad'].\n",
      "Only considering the two last: ['.0_ESM2_seed_29', '.h5ad'].\n"
     ]
    }
   ],
   "source": [
    "ad = sc.read(h5s[0])\n",
    "ad.obs[\"coarse\"] = [map_labels[a] for a in ad.obs[\"labels2\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f408cd44",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_species_knn_all_labels(adata, k=1, col = 'labels', metric='cosine',\n",
    "                          verbose = False):\n",
    "    \"\"\"Runs cross species k nearest neighbor on all cells\n",
    "    \"\"\"\n",
    "\n",
    "    \n",
    "    X = adata.X\n",
    "\n",
    "    # Slow step\n",
    "    if metric == 'euclidean':\n",
    "        dist_mat = euclidean_distances(X)\n",
    "    elif metric == 'cosine':\n",
    "        dist_mat = cosine_distances(X)\n",
    "        \n",
    "    all_nbrs = []\n",
    "    for l in adata.obs[\"labels\"].unique():\n",
    "        species_idx = np.where(adata.obs['labels']==l)[0]\n",
    "        s = adata[adata.obs['labels']==l].obs[\"species\"].unique()[0]\n",
    "        nonspecies_idx = np.where(adata.obs['species'] != s)[0]\n",
    "\n",
    "        # Slow step\n",
    "        reduced_dist_mat = dist_mat[species_idx,:][:,nonspecies_idx]\n",
    "\n",
    "        nns = [list(nonspecies_idx[y]) \n",
    "               for y in np.argpartition(reduced_dist_mat, k)[:,:k]]\n",
    "\n",
    "        nbrs = [(adata.obs[col][x], Mode(adata.obs[col][y].astype('str').values)) \n",
    "                        for x,y in zip(species_idx, nns)]\n",
    "        all_nbrs += nbrs\n",
    "    return all_nbrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62c0c393",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "h5s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bd27539",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "nbrs_dfs = []\n",
    "for h5 in tqdm(h5s):\n",
    "    ad = sc.read(h5)\n",
    "    ad.obs[\"coarse\"] = [map_labels[a] for a in ad.obs[\"labels2\"]]\n",
    "    ad_subset = ad[(ad.obs[\"labels2\"].str.lower().str.contains(\"beam\")) | (ad.obs[\"labels2\"].str.lower().str.contains(\"jct\")) | (ad.obs[\"labels2\"].str.lower().str.contains(\"uveal\"))]\n",
    "    all_nbrs = cross_species_knn_all_labels(ad_subset, k=1)\n",
    "    \n",
    "    all_cluster_nbrs  = [i for i in cluster_knn_all(all_nbrs)]\n",
    "    all_cluster_nbrs = pd.concat(all_cluster_nbrs).reset_index(drop=True)\n",
    "    \n",
    "    nbrs_dfs.append(all_cluster_nbrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9d526df",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nbrs_df = pd.concat(nbrs_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3eea222d",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = all_nbrs_df.groupby([\"Source_Cluster\", \"Cross_Species_KNN_Label\"]).sum().sort_values(\"Score\", ascending=False)\n",
    "grouped[\"Score\"] = grouped[\"Score\"] / 30"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec50d390",
   "metadata": {},
   "source": [
    "Disease genes in humans, which often demonstrated highly specific expression in outflow pathway cell types, mapped well in macaque species but less reliably in the pig and mouse. These results may help guide tests of therapeutic strategies in these models.\n",
    "\n",
    "\n",
    "Clusters 3 and 5 could be distinguished by preferential expression of FABP4 and TMEFF2, respectively, each of which marked a subset of beam cells, which we call Beam A and Beam B\n",
    "\n",
    "The FABP4+ Beam A cells and TMEFF2+ Beam B cells were intermingled but with a tendency for the latter to be closer to the JCT\n",
    "\n",
    "It is tempting to assign Beam A and B to uveal and corneoscleral layers, respectively, but our histological analysis suggests that they are in fact intermingled.\n",
    "\n",
    "\n",
    "Although it has been suggested that Schwalbe line contains TM precursors (34), we detected no differentially expressed genes suggestive of “stemness” (35) in this cluster; if present, they may have been too rare to be detected. This cluster had differentially expressed genes associated with corneal endothelium (e.g., CA3, MGARP, SLC11A2, TGFBI), \n",
    "\n",
    "\n",
    "\n",
    "This sample contained an additional vascular cell type (C18) and a fibroblastic type (C9) that were poorly represented in the other samples. Using the differentially expressed gene ACKR1 (DARC), we identified C18 as the endothelium of the CCs downstream from SC (Fig. 3F). A\n",
    "\n",
    "The third candidate TM cluster, mC9, despite sharing many markers with fibroblasts (e.g., Pi16, Fbn1, Mfap5, Tnxb, Clec3b), was closely related to mC14 and thus, tentatively named Beam Cell Y.\n",
    "\n",
    "\n",
    "Of note, whereas the Schwalbe line cluster in humans includes cells at the junction between cornea and TM, the corresponding clusters in pig and mouse more likely represent a larger proportion of corneal endothelia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3de8384b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_cluster_knn_bar(grouped.reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f048fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pig, Human and MacaF Beam A are the same cell type and map with Mouse Uveal\n",
    "# Mouse A, Y and MacaM A all map to eachother\n",
    "# MacaF Beam X, Human and Mouse and MacaM JCT map to eachother\n",
    "# MacaM Beam X, Human B, MacaF JCT, Pig JCT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1af978c7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "keep_cells = [\"beam\", \"Schwalbe\", \"JCT\", \"Fibroblast\", \"Uveal\"]\n",
    "ad_subset_new = ad[(ad.obs[\"coarse\"].isin(keep_cells)) | (ad.obs[\"labels2\"] == \"Corneal endothelium\")]\n",
    "sc.pp.pca(ad_subset_new)\n",
    "\n",
    "sc.pl.pca(ad_subset_new, color=\"coarse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae7012f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cl_vals_all = []\n",
    "for h5 in tqdm(h5s):\n",
    "    ad = sc.read(h5)\n",
    "    ad.obs[\"coarse\"] = [map_labels[a] for a in ad.obs[\"labels2\"]]\n",
    "\n",
    "    keep_cells = [\"beam\", \"Schwalbe\", \"JCT\", \"Fibroblast\", \"Uveal\"]\n",
    "    ad_subset_new = ad[(ad.obs[\"coarse\"].isin(keep_cells)) | (ad.obs[\"labels2\"] == \"Corneal endothelium\")]\n",
    "    sc.pp.pca(ad_subset_new)\n",
    "\n",
    "    sc.pp.neighbors(ad_subset_new)\n",
    "    sc.tl.leiden(ad_subset_new, resolution=0.05)\n",
    "    cf_table = sc.metrics.confusion_matrix(\"labels\", \"leiden\", ad_subset_new.obs)\n",
    "    cl_vals = cf_table.values @ cf_table.values.T\n",
    "    cl_vals_all.append(cl_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14e26b2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.metrics.confusion_matrix(\"labels\", \"leiden\", ad_subset_new.obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a19e39e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cl_avg_vals = np.average(np.stack(cl_vals_all, axis=2), axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba1a23ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "nice_cl_names = {\n",
    "    \"human_BeamCella\":\"Human Beam A\",\n",
    "    \"human_BeamCellb\":\"Human Beam B\",\n",
    "    \"human_CribiformJCT\":\"Human Cribiform JCT\",\n",
    "    \"human_Fibroblast\":\"Human Fibroblast\",\n",
    "    \"human_SchwalbeLine\":\"Human Schwalbe Line\",\n",
    "    \"macaca_fascicularis_15_Beam X\":\"Cynomolgus Macaque Beam X\",\n",
    "    \"macaca_fascicularis_2_BeamA\":\"Cynomolgus Macaque Beam A\",\n",
    "    \"macaca_fascicularis_6_JCT\":\"Cynomolgus Macaque JCT\",\n",
    "    \"macaca_fascicularis_7_Fibroblast\":\"Cynomolgus Macaque Fibroblast\",\n",
    "    \"macaca_mulatta_10_JCT\":\"Rhesus Macaque JCT\",\n",
    "    \"macaca_mulatta_1_Beam X\":\"Rhesus Macaque Beam X\",\n",
    "    \"macaca_mulatta_4_Beam A\":\"Rhesus Macaque Beam A\",\n",
    "    \"mouse_12_Corneal endothelium\":\"Mouse Corneal Endothelium\",\n",
    "    \"mouse_14_Beam A\":\"Mouse Beam A\",\n",
    "    \"mouse_1_Uveal\":\"Mouse Uveal\",\n",
    "    \"mouse_6_JCT\":\"Mouse JCT\",\n",
    "    \"mouse_9_Beam Y\":\"Mouse Beam Y\",\n",
    "    \"pig_17_Corneal endothelium\":\"Pig Corneal Endothelium\",\n",
    "    \"pig_1_Beam A\":\"Pig Beam A\",\n",
    "    \"pig_3_JCT\":\"Pig JCT\",\n",
    "    \"pig_6_Fibroblast\":\"Pig Fibroblast\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f532047a",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.DataFrame(cl_avg_vals)\n",
    "new_df.index = [nice_cl_names[a] for a in list(cf_table.index)]#cf_table.index\n",
    "new_df.columns = [nice_cl_names[a] for a in list(cf_table.index)]#list(new_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9aac6a8d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "#fig, ax = plt.subplots(sharex=False, sharey=False, figsize=(4, 2.25), frameon=False, dpi=300)\n",
    "ax = sns.clustermap(new_df, cmap=\"viridis\"); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee76ff0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cl_mapping = {\n",
    "    \"mouse_14_Beam A\":\"1\",\n",
    "    \"mouse_9_Beam Y\":\"1\",\n",
    "    \"pig_6_Fibroblast\":\"1\",\n",
    "    \"human_Fibroblast\":\"1\",\n",
    "    \"macaca_fascicularis_7_Fibroblast\":\"1\",\n",
    "    \n",
    "    \n",
    "    \"macaca_mulatta_4_Beam A\":\"2\",\n",
    "    \"mouse_1_Uveal\":\"2\",\n",
    "    \"human_BeamCella\":\"2\",\n",
    "    \"macaca_fascicularis_2_BeamA\":\"2\",\n",
    "    \"pig_1_Beam A\":\"2\",\n",
    "    \n",
    "    \"human_BeamCellb\":\"3\",\n",
    "    \"macaca_mulatta_1_Beam X\":\"3\",\n",
    "    \"macaca_fascicularis_6_JCT\":\"3\",\n",
    "    \"pig_3_JCT\":\"3\",\n",
    "    \n",
    "    \"macaca_fascicularis_15_Beam X\":\"4\",\n",
    "    \"macaca_mulatta_10_JCT\":\"4\",\n",
    "    \"human_CribiformJCT\":\"4\",\n",
    "    \"mouse_6_JCT\":\"4\",\n",
    "    \n",
    "    \"pig_17_Corneal endothelium\":\"5\",\n",
    "    \"human_SchwalbeLine\":\"5\",\n",
    "    \"mouse_12_Corneal endothelium\":\"5\",\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4b87b2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_cells = [\"beam\", \"Schwalbe\", \"JCT\", \"Fibroblast\", \"Uveal\"]\n",
    "h5 = h5s[5]\n",
    "\n",
    "ad = sc.read(h5)\n",
    "ad.obs[\"coarse\"] = [map_labels[a] for a in ad.obs[\"labels2\"]]\n",
    "\n",
    "ad_subset_new = ad[(ad.obs[\"coarse\"].isin(keep_cells)) | (ad.obs[\"labels2\"] == \"Corneal endothelium\")]\n",
    "sc.pp.pca(ad_subset_new)\n",
    "\n",
    "sc.pl.pca(ad_subset_new, color=\"coarse\")\n",
    "sc.pp.neighbors(ad_subset_new)\n",
    "sc.tl.umap(ad_subset_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a30a3e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ad_subset_new.obs[\"Reannotated Clusters\"] = [cl_mapping[ct] for ct in ad_subset_new.obs[\"labels\"]]\n",
    "sc.pl.umap(ad_subset_new, color=\"Reannotated Clusters\")\n",
    "sc.pl.umap(ad_subset_new, color=\"labels\")\n",
    "sc.pl.umap(ad_subset_new, color=\"species\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d9aff34",
   "metadata": {},
   "outputs": [],
   "source": [
    "h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b0cf6b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.tl.dendrogram(ad_subset_new, groupby=\"labels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "637d717f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pl.dendrogram(ad_subset_new, groupby=\"labels\", orientation=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf5f831e",
   "metadata": {},
   "outputs": [],
   "source": [
    "ancs_ad = sc.AnnData(ad_subset_new.obsm[\"ancs\"])\n",
    "ancs_ad.obsm = ad_subset_new.obsm\n",
    "ancs_ad.obs = ad_subset_new.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c3f055e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.tl.rank_genes_groups(ancs_ad, groupby=\"Reannotated Clusters\")\n",
    "#sc.pl.rank_genes_groups(ancs_ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d806e77a",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'h5' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [12]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpickle\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[43mh5\u001b[49m\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.h5ad\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_genes_to_centroids.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m),\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m      3\u001b[0m     genes_to_scores \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_scores\u001b[39m(anc_idx):\n",
      "\u001b[0;31mNameError\u001b[0m: name 'h5' is not defined"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "with open(h5.replace(\".h5ad\", \"_genes_to_centroids.pkl\"),'rb') as f:\n",
    "    genes_to_scores = pickle.load(f)\n",
    "def get_scores(anc_idx):\n",
    "    '''\n",
    "    Given the index of a centroid, return the scores by gene for that centroid\n",
    "    '''\n",
    "    scores = {}\n",
    "    for (gene), embs in genes_to_scores.items():\n",
    "        scores[gene] = embs[anc_idx]\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e28798a",
   "metadata": {},
   "outputs": [],
   "source": [
    "de = np.argmax(genes_to_scores[\"mouse_Myoc\"])\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ed5f8c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "de = np.argmax(genes_to_scores[\"human_MYOC\"])\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c9ffd65",
   "metadata": {},
   "outputs": [],
   "source": [
    "de = np.argmax(genes_to_scores[\"human_A2M\"])\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa6059db",
   "metadata": {},
   "outputs": [],
   "source": [
    "de = np.argmax(genes_to_scores[\"pig_A2M\"])\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fbb3abf",
   "metadata": {},
   "outputs": [],
   "source": [
    "de = np.argmax(genes_to_scores[\"human_TMEFF2\"])\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a070b22",
   "metadata": {},
   "outputs": [],
   "source": [
    "de = np.argmax(genes_to_scores[\"mouse_Tmeff2\"])\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bbe82ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "de = np.argmax(genes_to_scores[\"human_FABP4\"])\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66fd2a48",
   "metadata": {},
   "outputs": [],
   "source": [
    "de = np.argmax(genes_to_scores[\"human_CHI3L1\"])\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "262fb9f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "de = np.argmax(genes_to_scores[\"mouse_Fabp4\"])\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff7476cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "de = np.argmax(genes_to_scores[\"human_ANGPTL7\"])\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98ed2b61",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "sc.pl.umap(ancs_ad, color=f'{np.argmax(genes_to_scores[\"human_FABP4\"])}', title=\"Beam A FabP4\")\n",
    "sc.pl.umap(ancs_ad, color=f'{np.argmax(genes_to_scores[\"mouse_Tmeff2\"])}', title=\"Beam B TMEF2+\")\n",
    "sc.pl.umap(ancs_ad, color=f'{np.argmax(genes_to_scores[\"human_CHI3L1\"])}', title=\"JCT CHI3L1\")\n",
    "sc.pl.umap(ancs_ad, color=f'{np.argmax(genes_to_scores[\"human_ANGPTL7\"])}', title=\"JCT ANGPTL7\")\n",
    "\n",
    "\n",
    "sc.pl.umap(ancs_ad, color=\"species\", title=\"Species\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac5b196",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.tl.rank_genes_groups(ancs_ad, groupby=\"Reannotated Clusters\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a234cdf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pl.rank_genes_groups_dotplot(\n",
    "    ancs_ad,\n",
    "    n_genes=1,\n",
    "    values_to_plot=\"logfoldchanges\", cmap='viridis',\n",
    "    vmin=-4,\n",
    "    vmax=4,\n",
    "    min_logfoldchange=3,\n",
    "    colorbar_title='log fold change'\n",
    ")\n",
    "\n",
    "#sc.pl.rank_genes_groups_matrixplot(ancs_ad, n_genes=3, groupby=\"Reannotated Clusters\", values_to_plot ='pvals_adj')\n",
    "#sc.pl.rank_genes_groups_matrixplot(ancs_ad, n_genes=3, groupby=\"Reannotated Clusters\", values_to_plot ='logfoldchanges')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e44931e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for de in [152, 1386, 740, 1158, 1300]:\n",
    "    max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "    display(max_gene.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54a5f88f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ancs_ad_rank = sc.AnnData(np.log(ancs_ad.X))\n",
    "ancs_ad_rank.obs = ancs_ad.obs\n",
    "ancs_ad_rank.obsm = ancs_ad.obsm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fab0ffe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pl.umap(ancs_ad, color=\"232\", title=\"DE Cluster\", cmap=\"viridis\")\n",
    "sc.pl.umap(ancs_ad, color=\"Reannotated Clusters\", title=\"DE Cluster\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a25ea3",
   "metadata": {},
   "source": [
    "# MYOC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd2440f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pl.umap(ancs_ad[ancs_ad.obs[\"species\"] != \"human\"], color=f'{np.argmax(genes_to_scores[\"human_MYOC\"])}', title=\"Human MYOC Cluster\")\n",
    "sc.pl.umap(ancs_ad[ancs_ad.obs[\"species\"] != \"human\"], color=f'{np.argmax(genes_to_scores[\"human_MYOC\"])}', title=\"Human MYOC Cluster, Non Human Cells\")\n",
    "sc.pl.umap(ancs_ad[ancs_ad.obs[\"species\"] != \"human\"], color=\"species\", title=\"Non Human Cells\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25c545c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pl.umap(ancs_ad, color=f'{np.argmax(genes_to_scores[\"mouse_Myoc\"])}', title=\"Non Human MYOC Cluster\")\n",
    "sc.pl.umap(ancs_ad[ancs_ad.obs[\"species\"] == \"human\"], color=f'{np.argmax(genes_to_scores[\"mouse_Myoc\"])}', title=\"Non Human MYOC Cluster, Human Cells\")\n",
    "sc.pl.umap(ancs_ad[ancs_ad.obs[\"species\"] == \"human\"], color=\"species\", title=\"Human Cells\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23aed50e",
   "metadata": {},
   "source": [
    "# Figures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030dd454",
   "metadata": {},
   "source": [
    "# Figure 4A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c341705d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.neighbors(ad)\n",
    "sc.tl.umap(ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ae6f7975",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc._settings.settings._vector_friendly=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13b61f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(sharex=False, sharey=False, figsize=(4, 2.25), frameon=False, dpi=300)\n",
    "\n",
    "not_to_show = [\"Vascular\", \"Endothelium\", \"Schwalbe\", \n",
    "               \"Collector Channel\", \"Neuron\", \"Myoepithelium\", \n",
    "               \"Mast\", \"SC\"]\n",
    "ax = sc.pl.umap(ad, color=\"coarse\", show=False, alpha=1, legend_loc='on data', ax=ax, legend_fontsize=6, legend_fontoutline=1)\n",
    "ax.set(xlabel=None, ylabel=None);\n",
    "ax.set(title=None)\n",
    "for a in ax.texts:\n",
    "    if (a._text in not_to_show):\n",
    "        #print(a._text)\n",
    "        a.set_visible(False)\n",
    "    else:\n",
    "        a.set_text(a._text.title())\n",
    "    if a._text == 'Corneal':\n",
    "        a.set_text(\"Corneal\\nEpithelium\")\n",
    "    if a._text == 'Ciliary Epithelium':\n",
    "        a.set_text(\"Ciliary\\nEpithelium\")\n",
    "    if a._text == 'Collector Channel':\n",
    "        a.set_text(\"Collector\\nChannel\")\n",
    "    if a._text == 'Nkt':\n",
    "        a.set_text(\"NKT\")\n",
    "    a.set_text(\"\\n\".join(a._text.title().split()))\n",
    "    #if a._text == 'Macrophage':\n",
    "    #    a.set_text(\"3\")\n",
    "#ax.spines['top'].set_visible(False)\n",
    "#ax.spines['right'].set_visible(False)\n",
    "#ax.spines['bottom'].set_visible(False)\n",
    "#ax.spines['left'].set_visible(False)\n",
    "plt.savefig(\"figures/4a_main.svg\")    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af26b08b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(sharex=False, sharey=False, figsize=(4, 2.25), frameon=False, dpi=300)\n",
    "\n",
    "not_to_show = [\"Vascular\", \"Endothelium\", \"Schwalbe\", \n",
    "               \"Collector Channel\", \"Neuron\", \"Corneal\", \"Myoepithelium\", \n",
    "               \"Mast\", \"SC\"]\n",
    "ax = sc.pl.umap(ad, color=\"species\", show=False, alpha=1, legend_loc=None, ax=ax, legend_fontsize=7, legend_fontoutline=2)\n",
    "ax.set(xlabel=None, ylabel=None);\n",
    "ax.set(title=None)\n",
    "for a in ax.texts:\n",
    "    if (a._text in not_to_show):\n",
    "        #print(a._text)\n",
    "        a.set_visible(False)\n",
    "    if a._text == 'Spemann\\nOrganizer':\n",
    "        a.set_text(\"1\")\n",
    "    if a._text == 'Goblet\\nCell':\n",
    "        a.set_text(\"2\")\n",
    "    a.set_text(\"\\n\".join(a._text.title().split()))\n",
    "    #if a._text == 'Macrophage':\n",
    "    #    a.set_text(\"3\")\n",
    "#ax.spines['top'].set_visible(False)\n",
    "#ax.spines['right'].set_visible(False)\n",
    "#ax.spines['bottom'].set_visible(False)\n",
    "#ax.spines['left'].set_visible(False)\n",
    "plt.savefig(\"figures/4a_spec.svg\")    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d7c4ecf",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(sharex=False, sharey=False, figsize=(2.25, 2.25), frameon=False, dpi=300)\n",
    "\n",
    "not_to_show = [\"Vascular\", \"Endothelium\", \"Schwalbe\", \n",
    "               \"Collector Channel\", \"Neuron\", \"Corneal\", \"Myoepithelium\", \n",
    "               \"Mast\", \"SC\"]\n",
    "ax = sc.pl.umap(ancs_ad, color=\"Reannotated Clusters\", show=False, alpha=1, legend_loc='on data', ax=ax, legend_fontsize=7, \n",
    "                legend_fontoutline=2, palette=\"Set2\")\n",
    "ax.set(xlabel=None, ylabel=None);\n",
    "ax.set(title=None)\n",
    "for a in ax.texts:\n",
    "    if (a._text in not_to_show):\n",
    "        #print(a._text)\n",
    "        a.set_visible(False)\n",
    "    if a._text == 'Spemann\\nOrganizer':\n",
    "        a.set_text(\"1\")\n",
    "    if a._text == 'Goblet\\nCell':\n",
    "        a.set_text(\"2\")\n",
    "    a.set_text(\"\\n\".join(a._text.title().split()))\n",
    "    #if a._text == 'Macrophage':\n",
    "    #    a.set_text(\"3\")\n",
    "#ax.spines['top'].set_visible(False)\n",
    "#ax.spines['right'].set_visible(False)\n",
    "#ax.spines['bottom'].set_visible(False)\n",
    "#ax.spines['left'].set_visible(False)\n",
    "plt.savefig(\"figures/4b_leiden.svg\")    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51eaacff",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(sharex=False, sharey=False, figsize=(2.25, 2.25), frameon=False, dpi=300)\n",
    "\n",
    "not_to_show = [\"Vascular\", \"Endothelium\", \"Schwalbe\", \n",
    "               \"Collector Channel\", \"Neuron\", \"Corneal\", \"Myoepithelium\", \n",
    "               \"Mast\", \"SC\"]\n",
    "ax = sc.pl.umap(ancs_ad, color=\"species\", show=False, alpha=1, legend_loc=None, ax=ax, legend_fontsize=7, \n",
    "                legend_fontoutline=2)\n",
    "ax.set(xlabel=None, ylabel=None);\n",
    "ax.set(title=None)\n",
    "for a in ax.texts:\n",
    "    if (a._text in not_to_show):\n",
    "        #print(a._text)\n",
    "        a.set_visible(False)\n",
    "    if a._text == 'Spemann\\nOrganizer':\n",
    "        a.set_text(\"1\")\n",
    "    if a._text == 'Goblet\\nCell':\n",
    "        a.set_text(\"2\")\n",
    "    a.set_text(\"\\n\".join(a._text.title().split()))\n",
    "    #if a._text == 'Macrophage':\n",
    "    #    a.set_text(\"3\")\n",
    "#ax.spines['top'].set_visible(False)\n",
    "#ax.spines['right'].set_visible(False)\n",
    "#ax.spines['bottom'].set_visible(False)\n",
    "#ax.spines['left'].set_visible(False)\n",
    "plt.savefig(\"figures/4b_spec.svg\")    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccf193e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(sharex=False, sharey=False, figsize=(4, 2.25), frameon=False, dpi=300)\n",
    "\n",
    "sc.set_figure_params(scanpy=True, fontsize=10)\n",
    "dp_object = sc.pl.dendrogram(ad_subset_new, groupby=\"labels\", orientation=\"left\", ax=ax, show=False)\n",
    "\n",
    "axes_dict = dp_object\n",
    "# figure out which axis you want by printing the axes_dict\n",
    "# print(axes_dict)\n",
    "ax = axes_dict\n",
    "\n",
    "for label in ax.get_yticklabels():\n",
    "    label.set_fontsize(5)\n",
    "    t = label.get_text()\n",
    "    leiden = ancs_ad[ancs_ad.obs[\"labels\"] == t].obs[\"Reannotated Clusters\"].unique()[0]\n",
    "    label.set_color(ancs_ad.uns[\"Reannotated Clusters_colors\"][int(leiden) - 1])\n",
    "\n",
    "plt.savefig(\"figures/4b_dendrogram.svg\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb2095f9",
   "metadata": {},
   "source": [
    "# 4E Protein Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9de19a31",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "EMBEDDING_DIR = Path('/dfs/project/cross-species/data/proteome/embeddings')\n",
    "\n",
    "FZ_EMBEDDING_DIR = Path('/dfs/project/cross-species/yanay/data/proteome/embeddings')\n",
    "species_to_gene_embedding_path = {\n",
    "        'human': FZ_EMBEDDING_DIR / 'Homo_sapiens.GRCh38.gene_symbol_to_embedding_ESM2.pt',\n",
    "        'mouse': FZ_EMBEDDING_DIR / 'Mus_musculus.GRCm39.gene_symbol_to_embedding_ESM2.pt',\n",
    "        'frog': FZ_EMBEDDING_DIR / 'Xenopus_tropicalis.Xenopus_tropicalis_v9.1.gene_symbol_to_embedding_ESM2.pt',\n",
    "        #'frog': FZ_EMBEDDING_DIR / 'new_frog/xtropProtein.fasta.1.gene_symbol_to_embedding_ESM1b.pt',\n",
    "        'zebrafish': FZ_EMBEDDING_DIR / 'Danio_rerio.GRCz11.gene_symbol_to_embedding_ESM2.pt',\n",
    "        \"mouse_lemur\": FZ_EMBEDDING_DIR / \"Microcebus_murinus.Mmur_3.0.gene_symbol_to_embedding_ESM2.pt\",\n",
    "        \"pig\": FZ_EMBEDDING_DIR / 'Sus_scrofa.Sscrofa11.1.gene_symbol_to_embedding_ESM2.pt',\n",
    "        \"macaca_fascicularis\": FZ_EMBEDDING_DIR / 'Macaca_fascicularis.Macaca_fascicularis_6.0.gene_symbol_to_embedding_ESM2.pt',\n",
    "        \"macaca_mulatta\": FZ_EMBEDDING_DIR / 'Macaca_mulatta.Mmul_10.gene_symbol_to_embedding_ESM2.pt',\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a54a97ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "myoc_embeds = {}\n",
    "\n",
    "species_names = [\"human\", \"mouse\", \"pig\", \"macaca_fascicularis\", \"macaca_mulatta\", \"frog\", \n",
    "                 \"mouse_lemur\", \"zebrafish\"]\n",
    "vals = []\n",
    "gene_names = []\n",
    "species = []\n",
    "\n",
    "for s in species_names:\n",
    "    pts = torch.load(species_to_gene_embedding_path[s])\n",
    "    vals += list(pts.values())\n",
    "    gene_names += [s + \"_\" + a.lower() for a in list(pts.keys())]\n",
    "    species += [s]*len(list(pts.values()))\n",
    "    #pts = {k.lower(): v for k, v in pts.items()}\n",
    "    #myoc_embeds[species] = pts[\"myoc\"].numpy()\n",
    "vals = torch.stack(vals).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2c8c96d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "emb_ad = sc.AnnData(vals)\n",
    "emb_ad.obs[\"gene_names\"] = gene_names\n",
    "emb_ad.obs[\"species\"] = species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c92d7390",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [10]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpca\u001b[49m\u001b[43m(\u001b[49m\u001b[43memb_ad\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/scanpy/preprocessing/_pca.py:188\u001b[0m, in \u001b[0;36mpca\u001b[0;34m(data, n_comps, zero_center, svd_solver, random_state, return_info, use_highly_variable, dtype, copy, chunked, chunk_size)\u001b[0m\n\u001b[1;32m    184\u001b[0m         X \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mtoarray()\n\u001b[1;32m    185\u001b[0m     pca_ \u001b[38;5;241m=\u001b[39m PCA(\n\u001b[1;32m    186\u001b[0m         n_components\u001b[38;5;241m=\u001b[39mn_comps, svd_solver\u001b[38;5;241m=\u001b[39msvd_solver, random_state\u001b[38;5;241m=\u001b[39mrandom_state\n\u001b[1;32m    187\u001b[0m     )\n\u001b[0;32m--> 188\u001b[0m     X_pca \u001b[38;5;241m=\u001b[39m \u001b[43mpca_\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    189\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m issparse(X) \u001b[38;5;129;01mand\u001b[39;00m zero_center:\n\u001b[1;32m    190\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdecomposition\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PCA\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/sklearn/decomposition/_pca.py:433\u001b[0m, in \u001b[0;36mPCA.fit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    411\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    412\u001b[0m     \u001b[38;5;124;03m\"\"\"Fit the model with X and apply the dimensionality reduction on X.\u001b[39;00m\n\u001b[1;32m    413\u001b[0m \n\u001b[1;32m    414\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    431\u001b[0m \u001b[38;5;124;03m    C-ordered array, use 'np.ascontiguousarray'.\u001b[39;00m\n\u001b[1;32m    432\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 433\u001b[0m     U, S, Vt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    434\u001b[0m     U \u001b[38;5;241m=\u001b[39m U[:, : \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components_]\n\u001b[1;32m    436\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwhiten:\n\u001b[1;32m    437\u001b[0m         \u001b[38;5;66;03m# X_new = X * V / S * sqrt(n_samples) = U * sqrt(n_samples)\u001b[39;00m\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/sklearn/decomposition/_pca.py:485\u001b[0m, in \u001b[0;36mPCA._fit\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    483\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_full(X, n_components)\n\u001b[1;32m    484\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_svd_solver \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marpack\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandomized\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m--> 485\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_truncated\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_svd_solver\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    486\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    487\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    488\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnrecognized svd_solver=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_svd_solver)\n\u001b[1;32m    489\u001b[0m     )\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/sklearn/decomposition/_pca.py:597\u001b[0m, in \u001b[0;36mPCA._fit_truncated\u001b[0;34m(self, X, n_components, svd_solver)\u001b[0m\n\u001b[1;32m    595\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m svd_solver \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marpack\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    596\u001b[0m     v0 \u001b[38;5;241m=\u001b[39m _init_arpack_v0(\u001b[38;5;28mmin\u001b[39m(X\u001b[38;5;241m.\u001b[39mshape), random_state)\n\u001b[0;32m--> 597\u001b[0m     U, S, Vt \u001b[38;5;241m=\u001b[39m \u001b[43msvds\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mv0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    598\u001b[0m     \u001b[38;5;66;03m# svds doesn't abide by scipy.linalg.svd/randomized_svd\u001b[39;00m\n\u001b[1;32m    599\u001b[0m     \u001b[38;5;66;03m# conventions, so reverse its outputs.\u001b[39;00m\n\u001b[1;32m    600\u001b[0m     S \u001b[38;5;241m=\u001b[39m S[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/scipy/sparse/linalg/_eigen/_svds.py:339\u001b[0m, in \u001b[0;36msvds\u001b[0;34m(A, k, ncv, tol, which, v0, maxiter, return_singular_vectors, solver, random_state, options)\u001b[0m\n\u001b[1;32m    337\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m v0 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m rs_was_None:\n\u001b[1;32m    338\u001b[0m         v0 \u001b[38;5;241m=\u001b[39m random_state\u001b[38;5;241m.\u001b[39muniform(size\u001b[38;5;241m=\u001b[39m(\u001b[38;5;28mmin\u001b[39m(A\u001b[38;5;241m.\u001b[39mshape),))\n\u001b[0;32m--> 339\u001b[0m     _, eigvec \u001b[38;5;241m=\u001b[39m \u001b[43meigsh\u001b[49m\u001b[43m(\u001b[49m\u001b[43mXH_X\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtol\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmaxiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaxiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    340\u001b[0m \u001b[43m                      \u001b[49m\u001b[43mncv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mncv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhich\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhich\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mv0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    342\u001b[0m Av \u001b[38;5;241m=\u001b[39m X_matmat(eigvec)\n\u001b[1;32m    343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_singular_vectors:\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py:1689\u001b[0m, in \u001b[0;36meigsh\u001b[0;34m(A, k, M, sigma, which, v0, ncv, maxiter, tol, return_eigenvectors, Minv, OPinv, mode)\u001b[0m\n\u001b[1;32m   1687\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _ARPACK_LOCK:\n\u001b[1;32m   1688\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m params\u001b[38;5;241m.\u001b[39mconverged:\n\u001b[0;32m-> 1689\u001b[0m         \u001b[43mparams\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1691\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m params\u001b[38;5;241m.\u001b[39mextract(return_eigenvectors)\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py:547\u001b[0m, in \u001b[0;36m_SymmetricArpackParams.iterate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    544\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mido \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    545\u001b[0m     \u001b[38;5;66;03m# compute y = Op*x\u001b[39;00m\n\u001b[1;32m    546\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 547\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mworkd[yslice] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mOP\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mworkd\u001b[49m\u001b[43m[\u001b[49m\u001b[43mxslice\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    548\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m    549\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mworkd[xslice] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mOPb(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mworkd[xslice])\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/scipy/sparse/linalg/_interface.py:232\u001b[0m, in \u001b[0;36mLinearOperator.matvec\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m (N,) \u001b[38;5;129;01mand\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m (N,\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m    230\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdimension mismatch\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 232\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_matvec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, np\u001b[38;5;241m.\u001b[39mmatrix):\n\u001b[1;32m    235\u001b[0m     y \u001b[38;5;241m=\u001b[39m asmatrix(y)\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/scipy/sparse/linalg/_interface.py:530\u001b[0m, in \u001b[0;36m_CustomLinearOperator._matvec\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    529\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_matvec\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m--> 530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__matvec_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/scipy/sparse/linalg/_eigen/_svds.py:278\u001b[0m, in \u001b[0;36msvds.<locals>.matvec_XH_X\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmatvec_XH_X\u001b[39m(x):\n\u001b[0;32m--> 278\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m XH_dot(\u001b[43mX_dot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/scipy/sparse/linalg/_interface.py:232\u001b[0m, in \u001b[0;36mLinearOperator.matvec\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m (N,) \u001b[38;5;129;01mand\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m (N,\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m    230\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdimension mismatch\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 232\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_matvec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, np\u001b[38;5;241m.\u001b[39mmatrix):\n\u001b[1;32m    235\u001b[0m     y \u001b[38;5;241m=\u001b[39m asmatrix(y)\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/scipy/sparse/linalg/_interface.py:199\u001b[0m, in \u001b[0;36mLinearOperator._matvec\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    189\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_matvec\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m    190\u001b[0m     \u001b[38;5;124;03m\"\"\"Default matrix-vector multiplication handler.\u001b[39;00m\n\u001b[1;32m    191\u001b[0m \n\u001b[1;32m    192\u001b[0m \u001b[38;5;124;03m    If self is a linear operator of shape (M, N), then this method will\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;124;03m    will define matrix-vector multiplication as well.\u001b[39;00m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/scipy/sparse/linalg/_interface.py:337\u001b[0m, in \u001b[0;36mLinearOperator.matmat\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    333\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]:\n\u001b[1;32m    334\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdimension mismatch: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    335\u001b[0m                      \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape, X\u001b[38;5;241m.\u001b[39mshape))\n\u001b[0;32m--> 337\u001b[0m Y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_matmat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(Y, np\u001b[38;5;241m.\u001b[39mmatrix):\n\u001b[1;32m    340\u001b[0m     Y \u001b[38;5;241m=\u001b[39m asmatrix(Y)\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/scipy/sparse/linalg/_interface.py:731\u001b[0m, in \u001b[0;36mMatrixLinearOperator._matmat\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    730\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_matmat\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[0;32m--> 731\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mA\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "sc.pp.pca(emb_ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8343ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "emb_ad.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1ef6edd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "myoc_genes = emb_ad[(emb_ad.obs[\"gene_names\"].str.endswith(\"myoc\")) | (emb_ad.obs[\"gene_names\"].str.lower().str.contains(\"a2m\"))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a4b9176",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/user/21290/ipykernel_3239846/1074441599.py:1: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  myoc_genes.obs[\"Species\"] = pd.Categorical(myoc_genes.obs[\"species\"])\n"
     ]
    }
   ],
   "source": [
    "myoc_genes.obs[\"Species\"] = pd.Categorical(myoc_genes.obs[\"species\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "92d1c366",
   "metadata": {},
   "outputs": [],
   "source": [
    "myoc_genes.obs[\"Gene Names\"] = pd.Categorical(myoc_genes.obs[\"gene_names\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "394843ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lfs/ampere2/0/yanay/lib/python3.8/site-packages/anndata/_core/anndata.py:1785: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n",
      "  [AnnData(sparse.csr_matrix(a.shape), obs=a.obs) for a in all_adatas],\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 42 × 5120\n",
       "    obs: 'gene_names', 'species', 'Species', 'Gene Names', 'batch'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myoc_ad = myoc_genes.concatenate(myoc_genes)\n",
    "myoc_ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bf24bc18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 26 × 5120\n",
       "    obs: 'gene_names', 'species', 'Species', 'Gene Names', 'batch'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myoc_ad = myoc_ad[myoc_ad.obs[\"Species\"].isin(['human', 'mouse', 'pig', 'macaca_fascicularis', 'macaca_mulatta'])]\n",
    "myoc_ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2969f19f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['human', 'mouse', 'pig', 'macaca_fascicularis', 'macaca_mulatta', 'frog', 'mouse_lemur', 'zebrafish']\n",
       "Categories (8, object): ['frog', 'human', 'macaca_fascicularis', 'macaca_mulatta', 'mouse', 'mouse_lemur', 'pig', 'zebrafish']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myoc_genes.obs[\"Species\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "59bbe1b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 21 × 5120\n",
       "    obs: 'gene_names', 'species', 'Species', 'Gene Names'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myoc_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "49f1082d",
   "metadata": {},
   "outputs": [],
   "source": [
    "myoc_genes.obs[\"genes\"] = [g.replace(s + \"_\", \"\") for s, g in list(zip(list(myoc_genes.obs[\"Species\"]), list(myoc_genes.obs[\"Gene Names\"])))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f52bd02d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_names</th>\n",
       "      <th>species</th>\n",
       "      <th>Species</th>\n",
       "      <th>Gene Names</th>\n",
       "      <th>genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>human_a2m</td>\n",
       "      <td>human</td>\n",
       "      <td>human</td>\n",
       "      <td>human_a2m</td>\n",
       "      <td>a2m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>human_a2ml1</td>\n",
       "      <td>human</td>\n",
       "      <td>human</td>\n",
       "      <td>human_a2ml1</td>\n",
       "      <td>a2ml1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10430</th>\n",
       "      <td>human_myoc</td>\n",
       "      <td>human</td>\n",
       "      <td>human</td>\n",
       "      <td>human_myoc</td>\n",
       "      <td>myoc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20176</th>\n",
       "      <td>mouse_a2m</td>\n",
       "      <td>mouse</td>\n",
       "      <td>mouse</td>\n",
       "      <td>mouse_a2m</td>\n",
       "      <td>a2m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20177</th>\n",
       "      <td>mouse_a2ml1</td>\n",
       "      <td>mouse</td>\n",
       "      <td>mouse</td>\n",
       "      <td>mouse_a2ml1</td>\n",
       "      <td>a2ml1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31759</th>\n",
       "      <td>mouse_myoc</td>\n",
       "      <td>mouse</td>\n",
       "      <td>mouse</td>\n",
       "      <td>mouse_myoc</td>\n",
       "      <td>myoc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42115</th>\n",
       "      <td>pig_a2m</td>\n",
       "      <td>pig</td>\n",
       "      <td>pig</td>\n",
       "      <td>pig_a2m</td>\n",
       "      <td>a2m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42116</th>\n",
       "      <td>pig_a2ml1</td>\n",
       "      <td>pig</td>\n",
       "      <td>pig</td>\n",
       "      <td>pig_a2ml1</td>\n",
       "      <td>a2ml1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50579</th>\n",
       "      <td>pig_myoc</td>\n",
       "      <td>pig</td>\n",
       "      <td>pig</td>\n",
       "      <td>pig_myoc</td>\n",
       "      <td>myoc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57888</th>\n",
       "      <td>macaca_fascicularis_a2ml1</td>\n",
       "      <td>macaca_fascicularis</td>\n",
       "      <td>macaca_fascicularis</td>\n",
       "      <td>macaca_fascicularis_a2ml1</td>\n",
       "      <td>a2ml1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65926</th>\n",
       "      <td>macaca_fascicularis_myoc</td>\n",
       "      <td>macaca_fascicularis</td>\n",
       "      <td>macaca_fascicularis</td>\n",
       "      <td>macaca_fascicularis_myoc</td>\n",
       "      <td>myoc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73063</th>\n",
       "      <td>macaca_mulatta_a2ml1</td>\n",
       "      <td>macaca_mulatta</td>\n",
       "      <td>macaca_mulatta</td>\n",
       "      <td>macaca_mulatta_a2ml1</td>\n",
       "      <td>a2ml1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81976</th>\n",
       "      <td>macaca_mulatta_myoc</td>\n",
       "      <td>macaca_mulatta</td>\n",
       "      <td>macaca_mulatta</td>\n",
       "      <td>macaca_mulatta_myoc</td>\n",
       "      <td>myoc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90386</th>\n",
       "      <td>frog_a2m</td>\n",
       "      <td>frog</td>\n",
       "      <td>frog</td>\n",
       "      <td>frog_a2m</td>\n",
       "      <td>a2m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90387</th>\n",
       "      <td>frog_a2ml1</td>\n",
       "      <td>frog</td>\n",
       "      <td>frog</td>\n",
       "      <td>frog_a2ml1</td>\n",
       "      <td>a2ml1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97385</th>\n",
       "      <td>frog_myoc</td>\n",
       "      <td>frog</td>\n",
       "      <td>frog</td>\n",
       "      <td>frog_myoc</td>\n",
       "      <td>myoc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103350</th>\n",
       "      <td>mouse_lemur_a2m</td>\n",
       "      <td>mouse_lemur</td>\n",
       "      <td>mouse_lemur</td>\n",
       "      <td>mouse_lemur_a2m</td>\n",
       "      <td>a2m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103351</th>\n",
       "      <td>mouse_lemur_a2ml1</td>\n",
       "      <td>mouse_lemur</td>\n",
       "      <td>mouse_lemur</td>\n",
       "      <td>mouse_lemur_a2ml1</td>\n",
       "      <td>a2ml1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111212</th>\n",
       "      <td>mouse_lemur_myoc</td>\n",
       "      <td>mouse_lemur</td>\n",
       "      <td>mouse_lemur</td>\n",
       "      <td>mouse_lemur_myoc</td>\n",
       "      <td>myoc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121347</th>\n",
       "      <td>zebrafish_a2ml</td>\n",
       "      <td>zebrafish</td>\n",
       "      <td>zebrafish</td>\n",
       "      <td>zebrafish_a2ml</td>\n",
       "      <td>a2ml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130910</th>\n",
       "      <td>zebrafish_myoc</td>\n",
       "      <td>zebrafish</td>\n",
       "      <td>zebrafish</td>\n",
       "      <td>zebrafish_myoc</td>\n",
       "      <td>myoc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       gene_names              species              Species  \\\n",
       "2                       human_a2m                human                human   \n",
       "3                     human_a2ml1                human                human   \n",
       "10430                  human_myoc                human                human   \n",
       "20176                   mouse_a2m                mouse                mouse   \n",
       "20177                 mouse_a2ml1                mouse                mouse   \n",
       "31759                  mouse_myoc                mouse                mouse   \n",
       "42115                     pig_a2m                  pig                  pig   \n",
       "42116                   pig_a2ml1                  pig                  pig   \n",
       "50579                    pig_myoc                  pig                  pig   \n",
       "57888   macaca_fascicularis_a2ml1  macaca_fascicularis  macaca_fascicularis   \n",
       "65926    macaca_fascicularis_myoc  macaca_fascicularis  macaca_fascicularis   \n",
       "73063        macaca_mulatta_a2ml1       macaca_mulatta       macaca_mulatta   \n",
       "81976         macaca_mulatta_myoc       macaca_mulatta       macaca_mulatta   \n",
       "90386                    frog_a2m                 frog                 frog   \n",
       "90387                  frog_a2ml1                 frog                 frog   \n",
       "97385                   frog_myoc                 frog                 frog   \n",
       "103350            mouse_lemur_a2m          mouse_lemur          mouse_lemur   \n",
       "103351          mouse_lemur_a2ml1          mouse_lemur          mouse_lemur   \n",
       "111212           mouse_lemur_myoc          mouse_lemur          mouse_lemur   \n",
       "121347             zebrafish_a2ml            zebrafish            zebrafish   \n",
       "130910             zebrafish_myoc            zebrafish            zebrafish   \n",
       "\n",
       "                       Gene Names  genes  \n",
       "2                       human_a2m    a2m  \n",
       "3                     human_a2ml1  a2ml1  \n",
       "10430                  human_myoc   myoc  \n",
       "20176                   mouse_a2m    a2m  \n",
       "20177                 mouse_a2ml1  a2ml1  \n",
       "31759                  mouse_myoc   myoc  \n",
       "42115                     pig_a2m    a2m  \n",
       "42116                   pig_a2ml1  a2ml1  \n",
       "50579                    pig_myoc   myoc  \n",
       "57888   macaca_fascicularis_a2ml1  a2ml1  \n",
       "65926    macaca_fascicularis_myoc   myoc  \n",
       "73063        macaca_mulatta_a2ml1  a2ml1  \n",
       "81976         macaca_mulatta_myoc   myoc  \n",
       "90386                    frog_a2m    a2m  \n",
       "90387                  frog_a2ml1  a2ml1  \n",
       "97385                   frog_myoc   myoc  \n",
       "103350            mouse_lemur_a2m    a2m  \n",
       "103351          mouse_lemur_a2ml1  a2ml1  \n",
       "111212           mouse_lemur_myoc   myoc  \n",
       "121347             zebrafish_a2ml   a2ml  \n",
       "130910             zebrafish_myoc   myoc  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myoc_genes.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3b3e5263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 21 × 5120\n",
       "    obs: 'gene_names', 'species', 'Species', 'Gene Names', 'genes'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myoc_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b0b87202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a2m', 'a2ml1', 'myoc', 'a2ml'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myoc_genes.obs[\"genes\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82ed513a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "733e5738",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gene_subset' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [29]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mgene_subset\u001b[49m\u001b[38;5;241m.\u001b[39mobs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspecies\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'gene_subset' is not defined"
     ]
    }
   ],
   "source": [
    "gene_subset.obs[\"species\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9d8d56c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9f1391e3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'X_pca'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[0;32mIn [30]\u001b[0m, in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m gene \u001b[38;5;129;01min\u001b[39;00m spec5_subset\u001b[38;5;241m.\u001b[39mobs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenes\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39munique():\n\u001b[1;32m     15\u001b[0m     gene_subset \u001b[38;5;241m=\u001b[39m spec5_subset[spec5_subset\u001b[38;5;241m.\u001b[39mobs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenes\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m gene]\n\u001b[0;32m---> 16\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[43mgene_subset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobsm\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX_pca\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m[:, \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     17\u001b[0m     Y \u001b[38;5;241m=\u001b[39m gene_subset\u001b[38;5;241m.\u001b[39mobsm[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX_pca\u001b[39m\u001b[38;5;124m\"\u001b[39m][:, \u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     18\u001b[0m     specs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(gene_subset\u001b[38;5;241m.\u001b[39mobs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspecies\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/anndata/_core/aligned_mapping.py:113\u001b[0m, in \u001b[0;36mAlignedViewMixin.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m V:\n\u001b[1;32m    112\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m as_view(\n\u001b[0;32m--> 113\u001b[0m         _subset(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent_mapping\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubset_idx),\n\u001b[1;32m    114\u001b[0m         ElementRef(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparent, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname, (key,)),\n\u001b[1;32m    115\u001b[0m     )\n",
      "File \u001b[0;32m/lfs/ampere2/0/yanay/lib/python3.8/site-packages/anndata/_core/aligned_mapping.py:148\u001b[0m, in \u001b[0;36mAlignedActualMixin.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m V:\n\u001b[0;32m--> 148\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'X_pca'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#sc.pl.pca(myoc_genes.concatenate([myoc_genes]), color=\"Species\", show=False)\n",
    "\n",
    "\n",
    "xlim, ylim = (-1, 3), (-1.25, 0.5)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(sharex=False, sharey=False, figsize=(3, 2), frameon=False, dpi=300)\n",
    "\n",
    "markers = {'a2m':\"s\", 'a2ml1':\"^\", 'myoc':\"o\"}\n",
    "\n",
    "spec5_subset = myoc_genes[myoc_genes.obs[\"species\"].isin([\"human\", \"mouse\", \"pig\", \"macaca_fascicularis\", \"macaca_mulatta\"])]\n",
    "\n",
    "colors = {\"human\":\"#1f77b4\", \"mouse\":\"#d62728\", \"pig\":\"#9467bd\", \"macaca_fascicularis\":\"#ff7f0e\", \"macaca_mulatta\":\"#2ca02c\"}\n",
    "for gene in spec5_subset.obs[\"genes\"].unique():\n",
    "    gene_subset = spec5_subset[spec5_subset.obs[\"genes\"] == gene]\n",
    "    X = gene_subset.obsm[\"X_pca\"][:, 0]\n",
    "    Y = gene_subset.obsm[\"X_pca\"][:, 1]\n",
    "    specs = list(gene_subset.obs[\"species\"])\n",
    "    ax.scatter(x=X, y=Y, marker=markers[gene], c=[colors[s] for s in specs]);\n",
    "    #ax = sc.pl.pca(gene_subset, color=\"species\", show=False, alpha=1, ax=ax, legend_fontsize=5, \n",
    "    #                legend_fontoutline=2, size=150\n",
    "    #              )\n",
    "\n",
    "\n",
    "#ax.set_xlim(xlim)\n",
    "#ax.set_ylim(ylim)\n",
    "\n",
    "#ax.set(xlabel=None, ylabel=None);\n",
    "ax.set(title=None)\n",
    "for a in ax.texts:\n",
    "    #a.set_text(\"\\n\".join(a._text.title().split()))\n",
    "    a.set_text(a._text.replace(\"_\", \" \").title())\n",
    "    \n",
    "    #if a._text == 'Macrophage':\n",
    "    #    a.set_text(\"3\")\n",
    "#ax.spines['top'].set_visible(False)\n",
    "#ax.spines['right'].set_visible(False)\n",
    "#ax.spines['bottom'].set_visible(False)\n",
    "#ax.spines['left'].set_visible(False)\n",
    "\n",
    "ax.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False) \n",
    "\n",
    "ax.tick_params(\n",
    "    axis='y',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    right=False,      # ticks along the bottom edge are off\n",
    "    left=False,         # ticks along the top edge are off\n",
    "    labelleft=False) \n",
    "\n",
    "print(plt.xlim(), plt.ylim())\n",
    "plt.savefig(\"figures/4f_pca.svg\")\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1cb34f4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "human_a2m to 405\n",
      "human_a2ml1 to 1375\n",
      "human_myoc to 405\n",
      "mouse_a2m to 1375\n",
      "Error on mouse_a2ml1\n",
      "mouse_myoc to 664\n",
      "pig_a2m to 1375\n",
      "Error on pig_a2ml1\n",
      "pig_myoc to 664\n",
      "macaca_fascicularis_a2ml1 to 1375\n",
      "macaca_fascicularis_myoc to 664\n",
      "Error on macaca_mulatta_a2ml1\n",
      "macaca_mulatta_myoc to 664\n"
     ]
    }
   ],
   "source": [
    "all_caps_gene_to_scores = {k.upper():v for k,v in genes_to_scores.items()}\n",
    "for g in spec5_subset.obs[\"gene_names\"]:\n",
    "    try:\n",
    "        de = np.argmax(all_caps_gene_to_scores[g.upper()])\n",
    "        print(f\"{g} to {de}\")\n",
    "    except:\n",
    "        print(f\"Error on {g}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "994e5918",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ba0621d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ancs_ad_all = sc.AnnData(ad.obsm[\"ancs\"])\n",
    "ancs_ad_all.obsm = ad.obsm\n",
    "ancs_ad_all.obs = ad.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "12bc3785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "664"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(genes_to_scores[\"pig_MYOC\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "143739fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "405"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(genes_to_scores[\"human_MYOC\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5d152e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sc.tl.dendrogram(ancs_ad, groupby='labels', use_rep='X')\n",
    "#subset_1_ad.uns[\"dendrogram_labels2\"] = subset_1_ad_embs.uns[\"dendrogram_labels2\"]\n",
    "pig_myoc = np.argmax(genes_to_scores[\"pig_MYOC\"])\n",
    "human_myoc = np.argmax(genes_to_scores[\"human_MYOC\"])\n",
    "#human_a2m = np.argmax(genes_to_scores[\"macaF_A2ML1\"])\n",
    "pig_a2m = np.argmax(genes_to_scores[\"pig_A2M\"])\n",
    "\n",
    "markers = [f'{pig_myoc}', f'{human_myoc}', f'{pig_a2m}' ] #de_df_pos.head(5)[\"names\"].astype(str)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,3), dpi=300)\n",
    "dp = sc.pl.dotplot(ancs_ad, markers, groupby='species', dendrogram=False, show=False, cmap=\"viridis\", ax=ax, vmin=0.0, vmax=5, dot_min=0, dot_max=1)\n",
    "\n",
    "#dp[\"mainplot_ax\"].set_xticklabels([\"Arhgdi\", \"Ptp\", \"Parv\", \"Tmem\", \"Ssr\"], rotation = 0, ha=\"center\")\n",
    "dp[\"mainplot_ax\"].set_xticklabels([f\"Macrogene {pig_myoc}\\nNon Human Myoc\", \n",
    "                                   f\"Macrogene {human_myoc}\\nHuman Myoc/A2m \",\n",
    "                                   #f\"Macrogene {human_a2m}\\nHuman A2M \", \n",
    "                                   f\"Macrogene {pig_atm}\\nNon Human A2m\"], rotation = 90, ha=\"center\")\n",
    "dp[\"mainplot_ax\"].set_yticklabels([a.get_text().replace(\"_\", \" \").title() for a in dp[\"mainplot_ax\"].get_yticklabels()])\n",
    "\n",
    "plt.savefig(\"figures/4d_dot.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d166a6f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ad_non_subset = ad[(~(ad.obs[\"coarse\"].isin(keep_cells)) | (ad.obs[\"labels2\"] == \"Corneal endothelium\"))]\n",
    "ad_non_subset_anc = sc.AnnData(ad_non_subset.obsm[\"ancs\"])\n",
    "ad_non_subset_anc.obs = ad_non_subset.obs\n",
    "ad_non_subset_anc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a132db30",
   "metadata": {},
   "outputs": [],
   "source": [
    "genes_to_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5aa5d79",
   "metadata": {},
   "outputs": [],
   "source": [
    "pig_myoc = np.argmax(genes_to_scores[\"pig_MYOC\"])\n",
    "human_myoc = np.argmax(genes_to_scores[\"human_MYOC\"])\n",
    "#human_a2m = np.argmax(genes_to_scores[\"macaF_A2ML1\"])\n",
    "pig_a2m = np.argmax(genes_to_scores[\"pig_A2M\"])\n",
    "\n",
    "markers = [f'{pig_myoc}', f'{human_myoc}', f'{pig_a2m}' ] #de_df_pos.head(5)[\"names\"].astype(str)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,3), dpi=300)\n",
    "dp = sc.pl.dotplot(ad_non_subset_anc, markers, groupby='species', dendrogram=False, show=False, cmap=\"viridis\", ax=ax, vmin=0.0, vmax=5, dot_min=0, dot_max=1, )\n",
    "\n",
    "#dp[\"mainplot_ax\"].set_xticklabels([\"Arhgdi\", \"Ptp\", \"Parv\", \"Tmem\", \"Ssr\"], rotation = 0, ha=\"center\")\n",
    "dp[\"mainplot_ax\"].set_xticklabels([f\"Macrogene {pig_myoc}\\nNon Human Myoc\", \n",
    "                                   f\"Macrogene {human_myoc}\\nHuman Myoc/A2m \",\n",
    "                                   #f\"Macrogene {human_a2m}\\nHuman A2M \", \n",
    "                                   f\"Macrogene {pig_atm}\\nNon Human A2m\"], rotation = 90, ha=\"center\")\n",
    "dp[\"mainplot_ax\"].set_yticklabels([a.get_text().replace(\"_\", \" \").title() for a in dp[\"mainplot_ax\"].get_yticklabels()])\n",
    "\n",
    "#plt.savefig(\"figures/4d_dot.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71cb9665",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "#fig, ax = plt.subplots(sharex=False, sharey=False, figsize=(4, 2.25), frameon=False, dpi=300)\n",
    "ax = sns.clustermap(new_df, cmap=\"viridis\"); \n",
    "plt.savefig(\"figures/4b_heatmap.svg\")    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "55b6be4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "664\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8480</th>\n",
       "      <td>macaca_fascicularis_MYOC</td>\n",
       "      <td>1.394169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24410</th>\n",
       "      <td>mouse_Myoc</td>\n",
       "      <td>1.305283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16770</th>\n",
       "      <td>macaca_mulatta_MYOC</td>\n",
       "      <td>1.117631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>human_OLFML3</td>\n",
       "      <td>1.027965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35951</th>\n",
       "      <td>pig_MYOC</td>\n",
       "      <td>1.011485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8641</th>\n",
       "      <td>macaca_fascicularis_OLFML3</td>\n",
       "      <td>0.905605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32256</th>\n",
       "      <td>pig_GLDN</td>\n",
       "      <td>0.902806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4185</th>\n",
       "      <td>human_OLFML2A</td>\n",
       "      <td>0.896836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32643</th>\n",
       "      <td>pig_OLFML2A</td>\n",
       "      <td>0.870827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24450</th>\n",
       "      <td>mouse_Olfml2b</td>\n",
       "      <td>0.840055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             gene    weight\n",
       "8480     macaca_fascicularis_MYOC  1.394169\n",
       "24410                  mouse_Myoc  1.305283\n",
       "16770         macaca_mulatta_MYOC  1.117631\n",
       "406                  human_OLFML3  1.027965\n",
       "35951                    pig_MYOC  1.011485\n",
       "8641   macaca_fascicularis_OLFML3  0.905605\n",
       "32256                    pig_GLDN  0.902806\n",
       "4185                human_OLFML2A  0.896836\n",
       "32643                 pig_OLFML2A  0.870827\n",
       "24450               mouse_Olfml2b  0.840055"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de = np.argmax(genes_to_scores[\"mouse_Myoc\"])\n",
    "print(de)\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "17b6c9e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 50112 × 256\n",
       "    obs: 'labels', 'labels2', 'ref_labels', 'species', 'coarse'\n",
       "    obsm: 'ancs'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d62bb001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labels2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B cell</th>\n",
       "      <td>macaca_fascicularis,mouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCell</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beam A</th>\n",
       "      <td>macaca_mulatta,mouse,pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beam X</th>\n",
       "      <td>macaca_fascicularis,macaca_mulatta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beam Y</th>\n",
       "      <td>mouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BeamA</th>\n",
       "      <td>macaca_fascicularis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BeamCella</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BeamCellb</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ciliary muscle</th>\n",
       "      <td>macaca_fascicularis,macaca_mulatta,mouse,pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CiliaryMuscle</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Collector channel</th>\n",
       "      <td>macaca_fascicularis,pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CollectorChnlAqVein</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corneal</th>\n",
       "      <td>mouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corneal endothelium</th>\n",
       "      <td>mouse,pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corneal epithelium</th>\n",
       "      <td>macaca_fascicularis,mouse,pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CornealEpi</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CribiformJCT</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Endothelium</th>\n",
       "      <td>macaca_mulatta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibroblast</th>\n",
       "      <td>human,macaca_fascicularis,pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCT</th>\n",
       "      <td>macaca_fascicularis,macaca_mulatta,mouse,pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macrophage</th>\n",
       "      <td>human,macaca_fascicularis,macaca_mulatta,mouse...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MastCell</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melanocyte</th>\n",
       "      <td>human,macaca_fascicularis,macaca_mulatta,mouse...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Myelinating schwann cell</th>\n",
       "      <td>macaca_mulatta,pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Myoepithelium</th>\n",
       "      <td>mouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NK/T</th>\n",
       "      <td>macaca_mulatta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NK/T cell</th>\n",
       "      <td>macaca_fascicularis,macaca_mulatta,mouse,pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKT</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neuron</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nonmyelinating schwann cell</th>\n",
       "      <td>macaca_mulatta,pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nonpigmented ciliary epithelium</th>\n",
       "      <td>mouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pericyte</th>\n",
       "      <td>human,macaca_fascicularis,macaca_mulatta,mouse...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pigmented ciliary epithelium</th>\n",
       "      <td>mouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pigmented epithelium</th>\n",
       "      <td>mouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SChlemm's Canal</th>\n",
       "      <td>macaca_fascicularis,macaca_mulatta,mouse,pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScEndo</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SchwalbeLine</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schwann cell</th>\n",
       "      <td>macaca_fascicularis,mouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SchwannCell-my</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SchwannCell-nmy</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uveal</th>\n",
       "      <td>mouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vascular</th>\n",
       "      <td>macaca_fascicularis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vascular Endothelium</th>\n",
       "      <td>mouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VascularEndo</th>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                           species\n",
       "labels2                                                                           \n",
       "B cell                                                   macaca_fascicularis,mouse\n",
       "BCell                                                                        human\n",
       "Beam A                                                    macaca_mulatta,mouse,pig\n",
       "Beam X                                          macaca_fascicularis,macaca_mulatta\n",
       "Beam Y                                                                       mouse\n",
       "BeamA                                                          macaca_fascicularis\n",
       "BeamCella                                                                    human\n",
       "BeamCellb                                                                    human\n",
       "Ciliary muscle                        macaca_fascicularis,macaca_mulatta,mouse,pig\n",
       "CiliaryMuscle                                                                human\n",
       "Collector channel                                          macaca_fascicularis,pig\n",
       "CollectorChnlAqVein                                                          human\n",
       "Corneal                                                                      mouse\n",
       "Corneal endothelium                                                      mouse,pig\n",
       "Corneal epithelium                                   macaca_fascicularis,mouse,pig\n",
       "CornealEpi                                                                   human\n",
       "CribiformJCT                                                                 human\n",
       "Endothelium                                                         macaca_mulatta\n",
       "Fibroblast                                           human,macaca_fascicularis,pig\n",
       "JCT                                   macaca_fascicularis,macaca_mulatta,mouse,pig\n",
       "Macrophage                       human,macaca_fascicularis,macaca_mulatta,mouse...\n",
       "MastCell                                                                     human\n",
       "Melanocyte                       human,macaca_fascicularis,macaca_mulatta,mouse...\n",
       "Myelinating schwann cell                                        macaca_mulatta,pig\n",
       "Myoepithelium                                                                mouse\n",
       "NK/T                                                                macaca_mulatta\n",
       "NK/T cell                             macaca_fascicularis,macaca_mulatta,mouse,pig\n",
       "NKT                                                                          human\n",
       "Neuron                                                                       human\n",
       "Nonmyelinating schwann cell                                     macaca_mulatta,pig\n",
       "Nonpigmented ciliary epithelium                                              mouse\n",
       "Pericyte                         human,macaca_fascicularis,macaca_mulatta,mouse...\n",
       "Pigmented ciliary epithelium                                                 mouse\n",
       "Pigmented epithelium                                                         mouse\n",
       "SChlemm's Canal                       macaca_fascicularis,macaca_mulatta,mouse,pig\n",
       "ScEndo                                                                       human\n",
       "SchwalbeLine                                                                 human\n",
       "Schwann cell                                             macaca_fascicularis,mouse\n",
       "SchwannCell-my                                                               human\n",
       "SchwannCell-nmy                                                              human\n",
       "Uveal                                                                        mouse\n",
       "Vascular                                                       macaca_fascicularis\n",
       "Vascular Endothelium                                                         mouse\n",
       "VascularEndo                                                                 human"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad.obs[[\"species\", \"labels2\"]].groupby(\"labels2\").agg(lambda x: ','.join(x.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "21d496e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>labels2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coarse</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ciliary epithelium</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ciliary muscle</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Collector channel</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corneal</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Endothelium</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibroblast</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCT</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macrophage</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mast</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melanocyte</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Myoepithelium</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKT</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neuron</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pericyte</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schwalbe</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schwann</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uveal</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vascular</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b cell</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beam</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    species  labels2\n",
       "coarse                              \n",
       "Ciliary epithelium        1        3\n",
       "Ciliary muscle            5        2\n",
       "Collector channel         3        2\n",
       "Corneal                   4        4\n",
       "Endothelium               1        1\n",
       "Fibroblast                3        1\n",
       "JCT                       5        2\n",
       "Macrophage                5        1\n",
       "Mast                      1        1\n",
       "Melanocyte                5        1\n",
       "Myoepithelium             1        1\n",
       "NKT                       5        3\n",
       "Neuron                    1        1\n",
       "Pericyte                  5        1\n",
       "SC                        5        2\n",
       "Schwalbe                  1        1\n",
       "Schwann                   5        5\n",
       "Uveal                     1        1\n",
       "Vascular                  3        3\n",
       "b cell                    3        2\n",
       "beam                      5        6"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad.obs[[\"species\", \"coarse\", \"labels2\"]].groupby(\"coarse\").agg(lambda x: len(x.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dcc87c2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "405\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27657</th>\n",
       "      <td>mouse_Folr1</td>\n",
       "      <td>1.673962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>human_B2M</td>\n",
       "      <td>1.418661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27080</th>\n",
       "      <td>mouse_Fbln2</td>\n",
       "      <td>1.408592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28014</th>\n",
       "      <td>mouse_Srgn</td>\n",
       "      <td>1.316776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>human_MYOC</td>\n",
       "      <td>1.138967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26391</th>\n",
       "      <td>mouse_Sema3c</td>\n",
       "      <td>1.132521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26012</th>\n",
       "      <td>mouse_Lpar1</td>\n",
       "      <td>1.120726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31393</th>\n",
       "      <td>mouse_Adgrf5</td>\n",
       "      <td>0.885479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39103</th>\n",
       "      <td>pig_SCP2D1</td>\n",
       "      <td>0.813015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25867</th>\n",
       "      <td>mouse_Adgrl4</td>\n",
       "      <td>0.809624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               gene    weight\n",
       "27657   mouse_Folr1  1.673962\n",
       "5999      human_B2M  1.418661\n",
       "27080   mouse_Fbln2  1.408592\n",
       "28014    mouse_Srgn  1.316776\n",
       "632      human_MYOC  1.138967\n",
       "26391  mouse_Sema3c  1.132521\n",
       "26012   mouse_Lpar1  1.120726\n",
       "31393  mouse_Adgrf5  0.885479\n",
       "39103    pig_SCP2D1  0.813015\n",
       "25867  mouse_Adgrl4  0.809624"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de = np.argmax(genes_to_scores[\"human_MYOC\"])\n",
    "print(de)\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b579d38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "405\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27657</th>\n",
       "      <td>mouse_Folr1</td>\n",
       "      <td>1.673962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>human_B2M</td>\n",
       "      <td>1.418661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27080</th>\n",
       "      <td>mouse_Fbln2</td>\n",
       "      <td>1.408592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28014</th>\n",
       "      <td>mouse_Srgn</td>\n",
       "      <td>1.316776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>human_MYOC</td>\n",
       "      <td>1.138967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26391</th>\n",
       "      <td>mouse_Sema3c</td>\n",
       "      <td>1.132521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26012</th>\n",
       "      <td>mouse_Lpar1</td>\n",
       "      <td>1.120726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31393</th>\n",
       "      <td>mouse_Adgrf5</td>\n",
       "      <td>0.885479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39103</th>\n",
       "      <td>pig_SCP2D1</td>\n",
       "      <td>0.813015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25867</th>\n",
       "      <td>mouse_Adgrl4</td>\n",
       "      <td>0.809624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               gene    weight\n",
       "27657   mouse_Folr1  1.673962\n",
       "5999      human_B2M  1.418661\n",
       "27080   mouse_Fbln2  1.408592\n",
       "28014    mouse_Srgn  1.316776\n",
       "632      human_MYOC  1.138967\n",
       "26391  mouse_Sema3c  1.132521\n",
       "26012   mouse_Lpar1  1.120726\n",
       "31393  mouse_Adgrf5  0.885479\n",
       "39103    pig_SCP2D1  0.813015\n",
       "25867  mouse_Adgrl4  0.809624"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de = np.argmax(genes_to_scores[\"human_A2M\"])\n",
    "print(de)\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "532dcd50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1375\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23577</th>\n",
       "      <td>macaca_mulatta_C3</td>\n",
       "      <td>1.406938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27155</th>\n",
       "      <td>mouse_A2m</td>\n",
       "      <td>1.336448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38405</th>\n",
       "      <td>pig_A2M</td>\n",
       "      <td>1.059856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37706</th>\n",
       "      <td>pig_C4A</td>\n",
       "      <td>0.917377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31462</th>\n",
       "      <td>mouse_C3</td>\n",
       "      <td>0.902040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32193</th>\n",
       "      <td>pig_CD109</td>\n",
       "      <td>0.900841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34418</th>\n",
       "      <td>pig_C3</td>\n",
       "      <td>0.851106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080</th>\n",
       "      <td>macaca_mulatta_PZP</td>\n",
       "      <td>0.828825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23668</th>\n",
       "      <td>macaca_mulatta_CPAMD8</td>\n",
       "      <td>0.820771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21657</th>\n",
       "      <td>macaca_mulatta_C5</td>\n",
       "      <td>0.815449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        gene    weight\n",
       "23577      macaca_mulatta_C3  1.406938\n",
       "27155              mouse_A2m  1.336448\n",
       "38405                pig_A2M  1.059856\n",
       "37706                pig_C4A  0.917377\n",
       "31462               mouse_C3  0.902040\n",
       "32193              pig_CD109  0.900841\n",
       "34418                 pig_C3  0.851106\n",
       "20080     macaca_mulatta_PZP  0.828825\n",
       "23668  macaca_mulatta_CPAMD8  0.820771\n",
       "21657      macaca_mulatta_C5  0.815449"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de = np.argmax(genes_to_scores[\"pig_A2M\"])\n",
    "print(de)\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "303a61b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1375\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23577</th>\n",
       "      <td>macaca_mulatta_C3</td>\n",
       "      <td>1.406938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27155</th>\n",
       "      <td>mouse_A2m</td>\n",
       "      <td>1.336448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38405</th>\n",
       "      <td>pig_A2M</td>\n",
       "      <td>1.059856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37706</th>\n",
       "      <td>pig_C4A</td>\n",
       "      <td>0.917377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31462</th>\n",
       "      <td>mouse_C3</td>\n",
       "      <td>0.902040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32193</th>\n",
       "      <td>pig_CD109</td>\n",
       "      <td>0.900841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34418</th>\n",
       "      <td>pig_C3</td>\n",
       "      <td>0.851106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080</th>\n",
       "      <td>macaca_mulatta_PZP</td>\n",
       "      <td>0.828825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23668</th>\n",
       "      <td>macaca_mulatta_CPAMD8</td>\n",
       "      <td>0.820771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21657</th>\n",
       "      <td>macaca_mulatta_C5</td>\n",
       "      <td>0.815449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        gene    weight\n",
       "23577      macaca_mulatta_C3  1.406938\n",
       "27155              mouse_A2m  1.336448\n",
       "38405                pig_A2M  1.059856\n",
       "37706                pig_C4A  0.917377\n",
       "31462               mouse_C3  0.902040\n",
       "32193              pig_CD109  0.900841\n",
       "34418                 pig_C3  0.851106\n",
       "20080     macaca_mulatta_PZP  0.828825\n",
       "23668  macaca_mulatta_CPAMD8  0.820771\n",
       "21657      macaca_mulatta_C5  0.815449"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de = np.argmax(genes_to_scores[\"mouse_A2m\"])\n",
    "print(de)\n",
    "max_gene = pd.DataFrame(get_scores(int(de)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61d94007",
   "metadata": {},
   "source": [
    "# Fibroblast Markers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82622a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Your filename has more than two extensions: ['.0_pe_1', '.0_ESM2_seed_10', '.h5ad'].\n",
      "Only considering the two last: ['.0_ESM2_seed_10', '.h5ad'].\n",
      "WARNING: Your filename has more than two extensions: ['.0_pe_1', '.0_ESM2_seed_10', '.h5ad'].\n",
      "Only considering the two last: ['.0_ESM2_seed_10', '.h5ad'].\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cl_mapping = {\n",
    "    \"mouse_14_Beam A\":\"1\",\n",
    "    \"mouse_9_Beam Y\":\"1\",\n",
    "    \"pig_6_Fibroblast\":\"1\",\n",
    "    \"human_Fibroblast\":\"1\",\n",
    "    \"macaca_fascicularis_7_Fibroblast\":\"1\",\n",
    "    \n",
    "    \n",
    "    \"macaca_mulatta_4_Beam A\":\"2\",\n",
    "    \"mouse_1_Uveal\":\"2\",\n",
    "    \"human_BeamCella\":\"2\",\n",
    "    \"macaca_fascicularis_2_BeamA\":\"2\",\n",
    "    \"pig_1_Beam A\":\"2\",\n",
    "    \n",
    "    \"human_BeamCellb\":\"3\",\n",
    "    \"macaca_mulatta_1_Beam X\":\"3\",\n",
    "    \"macaca_fascicularis_6_JCT\":\"3\",\n",
    "    \"pig_3_JCT\":\"3\",\n",
    "    \n",
    "    \"macaca_fascicularis_15_Beam X\":\"4\",\n",
    "    \"macaca_mulatta_10_JCT\":\"4\",\n",
    "    \"human_CribiformJCT\":\"4\",\n",
    "    \"mouse_6_JCT\":\"4\",\n",
    "    \n",
    "    \"pig_17_Corneal endothelium\":\"5\",\n",
    "    \"human_SchwalbeLine\":\"5\",\n",
    "    \"mouse_12_Corneal endothelium\":\"5\",\n",
    "    \n",
    "}\n",
    "\n",
    "keep_cells = [\"beam\", \"Schwalbe\", \"JCT\", \"Fibroblast\", \"Uveal\"]\n",
    "h5 = h5s[5]\n",
    "\n",
    "ad = sc.read(h5)\n",
    "ad.obs[\"coarse\"] = [map_labels[a] for a in ad.obs[\"labels2\"]]\n",
    "\n",
    "ad_subset_new = ad[(ad.obs[\"coarse\"].isin(keep_cells)) | (ad.obs[\"labels2\"] == \"Corneal endothelium\")]\n",
    "sc.pp.pca(ad_subset_new)\n",
    "\n",
    "sc.pl.pca(ad_subset_new, color=\"coarse\")\n",
    "sc.pp.neighbors(ad_subset_new)\n",
    "sc.tl.umap(ad_subset_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65365ef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.pca(ad)\n",
    "sc.pp.neighbors(ad)\n",
    "sc.tl.umap(ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8cdf79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "anc_ad = sc.AnnData(ad.obsm[\"ancs\"])\n",
    "anc_ad.obs = ad.obs\n",
    "anc_ad.obsm = ad.obsm\n",
    "anc_ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9b9a13c",
   "metadata": {},
   "outputs": [],
   "source": [
    "anc_ad.obs[\"recluster\"] = [cl_mapping.get(l, \"Other\") for l in anc_ad.obs[\"labels\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "773ffef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pl.umap(anc_ad, color=\"recluster\")\n",
    "sc.pl.umap(anc_ad, color=)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5b1f3ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(h5.replace(\".h5ad\", \"_genes_to_centroids.pkl\"),'rb') as f:\n",
    "    genes_to_scores = pickle.load(f)\n",
    "def get_scores(anc_idx):\n",
    "    '''\n",
    "    Given the index of a centroid, return the scores by gene for that centroid\n",
    "    '''\n",
    "    scores = {}\n",
    "    for (gene), embs in genes_to_scores.items():\n",
    "        scores[gene] = embs[anc_idx]\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0c5b4f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1962\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5632</th>\n",
       "      <td>human_ITGBL1</td>\n",
       "      <td>0.924178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14863</th>\n",
       "      <td>macaca_fascicularis_FBN1</td>\n",
       "      <td>0.915157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24928</th>\n",
       "      <td>mouse_Fbn1</td>\n",
       "      <td>0.762170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11575</th>\n",
       "      <td>macaca_fascicularis_ITGBL1</td>\n",
       "      <td>0.750257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28984</th>\n",
       "      <td>mouse_Itgbl1</td>\n",
       "      <td>0.749282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             gene    weight\n",
       "5632                 human_ITGBL1  0.924178\n",
       "14863    macaca_fascicularis_FBN1  0.915157\n",
       "24928                  mouse_Fbn1  0.762170\n",
       "11575  macaca_fascicularis_ITGBL1  0.750257\n",
       "28984                mouse_Itgbl1  0.749282"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fbn1 = np.argmax(genes_to_scores[\"mouse_Fbn1\"])\n",
    "print(fbn1)\n",
    "max_gene = pd.DataFrame(get_scores(int(fbn1)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56f219a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pi16 = np.argmax(genes_to_scores[\"mouse_Pi16\"])\n",
    "print(pi16)\n",
    "max_gene = pd.DataFrame(get_scores(int(pi16)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cc8e908",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mfap5 = np.argmax(genes_to_scores[\"mouse_Mfap5\"])\n",
    "print(Mfap5)\n",
    "max_gene = pd.DataFrame(get_scores(int(Mfap5)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5faca791",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Tnxb = np.argmax(genes_to_scores[\"mouse_Tnxb\"])\n",
    "print(Tnxb)\n",
    "max_gene = pd.DataFrame(get_scores(int(Tnxb)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5dea435",
   "metadata": {},
   "outputs": [],
   "source": [
    "Clec3b = np.argmax(genes_to_scores[\"mouse_Clec3b\"])\n",
    "print(Clec3b)\n",
    "max_gene = pd.DataFrame(get_scores(int(Clec3b)).items(), columns=[\"gene\", \"weight\"]).sort_values(\"weight\", ascending=False)\n",
    "max_gene.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61542563",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sc.pl.umap(anc_ad, color=\"recluster\")\n",
    "sc.pl.umap(anc_ad, color=\"species\")\n",
    "fibroblast_genes = {\n",
    "    \"Fbn1\":fbn1,\n",
    "    \"Pi16\":pi16,\n",
    "    \"Mfap5\":Mfap5,\n",
    "    \"Tnxb\":Tnxb,\n",
    "    \"Clec3b\":Clec3b,\n",
    "}\n",
    "\n",
    "sc.pl.umap(anc_ad, color=[str(g) for g in fibroblast_genes.values()])\n",
    "\n",
    "for g in fibroblast_genes:\n",
    "    print(g)\n",
    "    sc.pl.dotplot(anc_ad, var_names=[str(fibroblast_genes[g])], groupby=\"recluster\", cmap=\"viridis\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f81b43",
   "metadata": {},
   "source": [
    "# Biohub Poster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "aeb804bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.pca(ad)\n",
    "sc.pp.neighbors(ad)\n",
    "sc.tl.umap(ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bc059982",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = 4#9.3\n",
    "height = 3#4.9\n",
    "fig, ax = plt.subplots(sharex=False, sharey=False, figsize=(width, height), frameon=False, dpi=300)\n",
    "\n",
    "not_to_show = [\"Vascular\", \"Endothelium\", \"Schwalbe\", \n",
    "               \"Collector Channel\", \"Neuron\", \"Myoepithelium\", \n",
    "               \"Mast\", \"SC\"]\n",
    "ax = sc.pl.umap(ad, color=\"coarse\", show=False, alpha=1, legend_loc='on data', ax=ax, legend_fontsize=6, legend_fontoutline=1)\n",
    "ax.set(xlabel=None, ylabel=None);\n",
    "ax.set(title=None)\n",
    "for a in ax.texts:\n",
    "    if (a._text in not_to_show):\n",
    "        #print(a._text)\n",
    "        a.set_visible(False)\n",
    "    else:\n",
    "        a.set_text(a._text.title())\n",
    "    if a._text == 'Corneal':\n",
    "        a.set_text(\"Corneal\\nEpithelium\")\n",
    "    if a._text == 'Ciliary Epithelium':\n",
    "        a.set_text(\"Ciliary\\nEpithelium\")\n",
    "    if a._text == 'Collector Channel':\n",
    "        a.set_text(\"Collector\\nChannel\")\n",
    "    if a._text == 'Nkt':\n",
    "        a.set_text(\"NKT\")\n",
    "    #if a._text == 'Macrophage':\n",
    "    #    a.set_text(\"3\")\n",
    "    #a.set_fontsize(12)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "plt.savefig(\"figures/4a_main_poster.svg\")    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a02f291e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(sharex=False, sharey=False, figsize=(width, height), frameon=False, dpi=300)\n",
    "\n",
    "not_to_show = [\"Vascular\", \"Endothelium\", \"Schwalbe\", \n",
    "               \"Collector Channel\", \"Neuron\", \"Myoepithelium\", \n",
    "               \"Mast\", \"SC\"]\n",
    "ax = sc.pl.umap(ad, color=\"species\", show=False, alpha=1, legend_loc=None, ax=ax, legend_fontsize=7, legend_fontoutline=2)\n",
    "ax.set(xlabel=None, ylabel=None);\n",
    "ax.set(title=None)\n",
    "for a in ax.texts:\n",
    "    if (a._text in not_to_show):\n",
    "        #print(a._text)\n",
    "        a.set_visible(False)\n",
    "    if a._text == 'Spemann\\nOrganizer':\n",
    "        a.set_text(\"1\")\n",
    "    if a._text == 'Goblet\\nCell':\n",
    "        a.set_text(\"2\")\n",
    "    #a.set_text(\"\\n\".join(a._text.title().split()))\n",
    "    #if a._text == 'Macrophage':\n",
    "    #    a.set_text(\"3\")\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "plt.savefig(\"figures/4a_spec_poster.svg\")    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a06dcdfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "myoc_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4560c6d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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